[Purpose/Significance] Against the backdrop of a strategic transition from industrial efficiency to embodied intelligence within the "Silver-haired Economy," social robots are evolving from functional tools into social companions. However, the field faces a critical bottleneck: a lack of interaction stickiness and empathetic resonance, which leads to high abandonment rates. Affective computing (AC) serves as the core technology to bridge this gap by enabling machines to detect, interpret, and simulate human emotions. Unlike previous literature that often treats AC as a standalone algorithmic task, this research reconstructs the value of AC from a Human-AI Interaction (HAI) perspective. This approach responds to the national "15th Five-Year Plan" requirements for secure and controllable AI governance by integrating technical pathways with ethical boundaries. By situating social robots within complex social relationships, this study provides a theoretical roadmap for robots to transition from mechanical entities to responsible social agents, thereby supporting the high-quality development of population-centric services. [Method/Process] This study employs a systematic literature review methodology guided by the PRISMA framework to ensure scientific rigor and comprehensiveness. The Web of Science Core Collection served as the primary data source, with a search timeframe spanning from 2015 to 2025 to capture the paradigm shifts triggered by deep learning and large-scale language models. A tripartite search logic-integrating subject entities (social robots), core technologies (affective computing), and interaction contexts (human-robot interaction)-was implemented to filter relevant literature. After a multi-level screening process based on embodiment, technical integrity, and empirical validity, 97 high-quality articles were selected. The study utilizes CiteSpace for keyword clustering and citation burst analysis, mapping the evolution of the field across three distinct stages: from foundational signal processing (2018-2019) to dynamic adaptation models (2020-2022), and finally to generative-driven intelligence and ethical regulation (2023-2025). This systematic approach allows for a deep synthesis of multimodal perception technologies, including robust vision, paralinguistic decoding, and physiological signal sensing. [Results/Conclusions] The findings reveal a significant paradigm shift in affective computing for social robots, evolving from simple signal statistics to deep situational understanding and from static rule-based responses to generative dynamic adaptation. The research proposes a holistic interaction framework comprising three pillars: situational understanding, adaptive action, and ethical constraints. Situational understanding leverages multimodal semantic fusion to decode human intent beyond surface-level data, while adaptive action ensures cross-modal consistency in physical expression through generative AI and long-term memory architectures. Ethical constraints are identified as an internal safety mechanism rather than external regulations, addressing risks such as privacy asymmetry, cultural bias in datasets, and psychological manipulation stemming from high anthropomorphism. The study concludes that the future of social robotics lies in three innovative paradigms: enhancing ecological validity through real-world deployment, constructing lifelong learning mechanisms to sustain long-term relationships, and embedding "human-in-the-loop" ethical fuses directly into algorithmic architectures. Despite these advancements, the research is currently limited by a lack of diverse cultural data and long-term field studies. Future research should prioritize cross-cultural design and the development of explainable affective decision-making modules to ensure the sustainable and benevolent development of embodied intelligence in complex social environments.
[Purpose/Significance] With the continuous advancement of national governance modernization and the rapid development of artificial intelligence (AI) technologies, emotional-functional embodied intelligence has become integral to grassroots social governance. This development not only reshapes traditional governance tools but also triggers profound reflections on the balance between instrumental rationality and value rationality. In this context, systematically examining the internal mechanisms and potential risks associated with the integration of emotional-functional embodied intelligence into social governance can provide both theoretical enrichment and practical guidance for technology-enabled governance modernization. [Method/Process] Based on Max Weber's "tool-value" dichotomy, this study focuses on key issues concerning the influence mechanisms, risk boundaries, and regulatory pathways of emotional-functional embodied intelligence in social governance. By situating the analysis within concrete scenarios of its embedding in social governance practices, the research combines theoretical reflection with contextual examination to explore how emotional-functional embodied intelligence reshapes governance structures and processes. [Results/Conclusions] The findings reveal that AI, embodied intelligence, and emotional-functional embodied intelligence differ significantly in terms of technological architecture, functional form, and modes of integration into social governance. While AI optimizes decision-making through data empowerment and embodied intelligence delivers services through physical interaction, emotional-functional embodied intelligence achieves full-process and in-depth integration into social governance by relying on affective linkage. It forms an integrated structural system composed of the demand, intelligence, action, and support layers, thereby enabling coordinated governance operations that combine rational decision-making with emotional interaction. Through three core mechanisms - intelligence embedding, human-machine coupling and feedback-driven iteration, emotional-functional embodied intelligence is able to simultaneously accomplish rational decision-making tasks and emotional interaction objectives. However, the embedding of emotional-functional embodied intelligence in social governance also implies dual structure of risks. On one hand, it may amplify traditional risks inherent in AI technologies, such as algorithmic dependence and blurred responsibility attribution. On the other hand, it may generate new forms of context-specific risks, including emotional-cognitive alienation, value-guidance deviation, and the reconstruction of governance authority. To address these challenges, it is necessary to construct a full-chain regulatory framework for accountability and establish full-process technological safeguards encompassing ex-ante prevention, in-process monitoring, and ex-post traceability. Concurrently,it's essential to articulate value-oriented principles for emotion-informed governance and clarify a human-machine collaborative governance framework in which human actors retain primary authority while intelligent technologies play an auxiliary role. Through these coordinated measures, effective risk regulation and rational balance can be achieved in the application of emotional-functional embodied intelligence in social governance.
[Purpose/Significance] Under the background of the digital economy, problems such as the difficulty in integrating multi-source heterogeneous data, low efficiency in matching supply and demand, and imbalance between security and openness in library data opening and sharing have restricted traditional technologies and service models from breaking through the bottlenecks. Large language models (LLMs) offer a new path to break through this predicament. This study aims to improve the theoretical system of technology that empowers the open sharing of library data. It also aims to fill the gap in existing research, which mostly focuses on general technologies and lacks systematic adaptation to library scenarios. Additionally, this study aims to provide theoretical and practical support for libraries to transform from data custodians to knowledge enablers, which will support the high-quality development of the industry. [Method/Process] Based on the elaboration of the practical impact of LLMs on the open sharing of library data, this paper analyzed the connotation, essence and characteristics of library data open sharing empowered by LLMs Based on this, the internal logic of LLMs driving the open sharing of library data was discussed, and the implementation path was explored. [Results/Conclusions] The open sharing of library data based on LLMs is manifested as a hierarchical leap in the value of data elements from basic integration, demand matching to decision support. This process needs to be efficiently advanced through human-machine collaboration on the supply side, user participation on the demand side, and cross-domain linkage on the ecosystem side. It should run through the entire life cycle of data production, governance, circulation, and application. Based on this, four guarantee strategies were proposed. In terms of technical architecture, we should adopt the "general model + domain fine-tuning" mode to adapt to the characteristics of library data. Efforts should be devoted to establishing a full-process quality control and hierarchical desensitization mechanism in data governance. In terms of talent cultivation, we should build a "business + discipline + technology" compound team. In terms of ethical construction, a full-process review and user rights protection system should be established. In the future, it is possible to further explore the in-depth adaptation of LLMs with the special collection resources of libraries, as well as the construction of a dynamic and elastic security governance framework, to promote the ecological development of industry data openness and sharing.
[Purpose/Significance] Starting from the perspective of technological complementarity, this paper proposes a new approach for identifying technological opportunities by comprehensively using outlier patents and hot patents. The fusion analysis of innovative outlier patents and market mature hot patents is carried out to identify "innovation maturity" technological opportunities that combineinnovation and maturity, which is of great significance for enriching the theory and methods of technological opportunityidentification. [Method/Process] First, based on the "association distribution" characteristics of patent classification numbers, a twostagemethod was adopted to screen patents. In the first stage, we used the association rule algorithms to find classification numberswith weak and strong associations, and obtained initial outlier patents and initial hotspot patents. In the second stage, outlier detectionalgorithms were used to obtain the marginalization classification numbers of the two types of patents in the first stage. Patentscontaining marginalization classification numbers were selected as the final outlier patents, while patents containing suchclassification numbers were removed as the final hotspot patents. Second, different methods were adopted for patent screening basedon the differences in innovation and maturity of patent content. Using structured and unstructured data from patent databases, weconstructed time weighted indicators and keyword uniqueness indicators as the screening indicators for innovative outlier patents. Weconstructed a technology lifecycle stage discrimination function and patent market value measurement indicators as the screeningcriteria for mature hot patents in the market. The screened patents were classified into technical fields based on the major categories inthe International Patent Classification. Finally, we identified technological opportunities based on technological complementarity. Byusing the generative topology mapping algorithm to obtain a technical blank point map, the top K keywords in each blank point wereobtained, and the sources of the keywords were marked to ensure that new technological opportunities have both good innovationcapabilities and mature market prospects. In the future, keyword combinations derived from different types of patents were regardedas "innovation mature" technological opportunities. [Results/Conclusions] Taking the field of new energy vehicle batteries as anexample, empirical analysis was conducted to obtain a total of 10 technical opportunities in 5 sub technical fields. Through contentcomparison with relevant policy texts, 7 technical opportunities showed high consistency. It was found that the identification resultswere highly consistent with the current technological layout and development direction of the field, indicating that this method hasgood effectiveness and scientificity in technology opportunity identification, and can provide support for technology prediction andinnovation decision-making.
[Purpose/Significance] The rapid expansion of artificial intelligence generated content (AIGC) is transforming how intellectual property (IP) literacy is cultivated in universities. Conventional approaches, often constrained by disciplinary fragmentation, uneven teaching capacity, and time–space limitations, are increasingly misaligned with human-AI collaborative learning. Against this backdrop, IP literacy must integrate legal knowledge, ethical judgment, compliance awareness, and AI-enabled creative practice. This study clarifies the renewed connotations of IP literacy in the AIGC era, develops a theoretically grounded model of influencing factors, and examines how multiple educational conditions combine to generate high-level outcomes. By focusing on IP literacy rather than generic digital competence, the paper addresses a clear gap in existing research and offers a configuration-based understanding that links theory to implementable strategies for intelligent, student-centered IP literacy education. [Method/Process] Grounded in Activity Theory, the study developed a six-dimensional framework consisting of the following variables: teacher professional competence, AI-IP awareness, diversified educational support, role division, evaluation mechanisms, and AI resources. These variables were operationalized via a structured questionnaire. Fuzzy-set Qualitative Comparative Analysis (fsQCA) was then employed to identify conjunctural causality and equifinal pathways that extend beyond linear models. High-outcome configurations were achieved through variable calibration, truth-table analysis, and minimization. Robustness was confirmed by tightening the PRI consistency threshold from 0.80 to 0.85. The path structure, overall coverage, and overall consistency remained stable. [Results/Conclusions] Findings show that AIGC-enabled IP literacy emerges through multiple effective configurational paths, rather than a single dominant factor. Across high-outcome configurations, teacher professional competence, AI–IP awareness, and diversified educational support consistently function as core drivers that shape learning processes and outcomes. Evaluation mechanisms and AI resources act as complementary or substitutive conditions, reinforcing effectiveness under specific institutional and resource constraints. Three typical paths were identified: a path emphasizing practice generation coupled with collaborative organization; a path that integrates resource sharing with practice-oriented development; and a path highlighting collaborative division of labor and effective communication to compensate for limited technical supply. Together, these paths confirm the internal logic of the six-dimensional model and demonstrate that coordinated configurations, rather than isolated improvements, are necessary to optimize IP literacy education in AI-rich contexts. Practical implications include strengthening AI-oriented teacher development, embedding AI-IP awareness in curricula and supporting services, building cross-unit collaboration mechanisms, and aligning role division and process evaluation with available AI resources. Although the cross-sectional design and limited scope constrain generalizability, the results provide a theoretically grounded and empirically supported basis for developing intelligent, collaborative, and student-centered IP literacy systems and offer a foundation for future longitudinal and comparative research in AIGC-enabled higher education.
[Purpose/Significance] The effective flow of agricultural knowledge from innovation sources to fields is a core component of agricultural modernization. However, a persistent "structural knowledge gap" exists between macro-level knowledge supply and the micro-level needs of farmers, which traditional top-down extension systems often fail to bridge due to issues such as information decay, a lack of feedback, and poor contextual adaptation. In the context of promoting the high-quality development of rural public cultural services, grassroots reading spaces (e.g., rural libraries and village reading rooms) face a critical imperative to evolve beyond their traditional role as static repositories of books. This study reimagines grassroots reading spaces as dynamic "knowledge nodes" within rural socio-information ecosystems. The primary significance of this research lies in its innovative integration of public governance and knowledge management theories to construct a novel "node-interface-flow" analytical framework. It moves the discourse forward from predominant concerns with resource allocation or technology access to a deeper investigation of how internal governance mechanisms fundamentally shape these spaces' capacity to process and diffuse knowledge. By doing so, it positions the study at the intersection of rural studies, public administration, and knowledge science, offering a refined theoretical lens to understand and design rural knowledge infrastructure. Its practical importance stems from providing evidence-based, mechanistic explanations and actionable pathways for transforming these ubiquitous facilities from venues of "cultural provision" into active agents of "knowledge empowerment" for rural communities. [Method/Process] To uncover the mechanisms through which collaborative governance influences knowledge flow, this study employed a sequential explanatory mixed-methods design (QUAN → QUAL). The research was empirically grounded in a comparative case study of three rural reading spaces in China, deliberately selected through theoretical sampling to represent three distinct ideal-typical governance models: Jiangyin (exemplifying a deep contractual model involving long-term institutional agreements between local government and a vocational college), Liancheng (representing an administrative-dominant model operating within a standardized county-branch library system), and Yuhang (illustrating a social collaborative model based on government-purchased services from local social organizations). The methodological appropriateness of this multi-case comparative approach lies in its capacity to maximize variation in the key independent variable (governance model) while controlling for contextual factors, thereby allowing for clearer causal inference regarding the model's impact. Data were collected from March to August of 2024. The quantitative phase involved a structured questionnaire survey administered to 438 farmers across the villages served by the three case spaces (from 480 distributed, 91.3% valid response rate). The survey instrument was designed to measure key variables derived from the theoretical framework, including perceived interface quality (e.g., resource relevance, expert accessibility), knowledge acquisition, community knowledge sharing, and technology adoption intention. Reliability and validity tests (e.g., Cronbach's α, K-R20) confirmed the robustness of the measures. The subsequent qualitative phase comprised 38 in-depth, semi-structured interviews with space managers, active farmers, and key partners, supplemented by participatory observation and archival analysis. This phase aimed to provide rich, contextual insights into the operational mechanisms linking governance rules, interface functioning, and knowledge flow patterns. Quantitative data were analyzed using SPSS for ANOVA and regression analysis to test performance differences and mediation effects, while qualitative data were thematically coded using NVivo to elucidate underlying processes. [Results/Conclusions] The findings confirm the proposed "governance model → interface characteristics → flow efficacy" mechanism. The deep contractual model, through its "embedded interface," successfully couples strong formal institutional guarantees (e.g., mandated expert deployment, resource co-selection) with derived informal trust relationships from long-term embeddedness. This combination significantly drives the deep, closed-loop flow of highly complex, codified knowledge, completing cycles from external input to local application and feedback. In contrast, the social collaborative model's "networked interface," characterized by vibrant informal community networks activated by skilled social organizers, proves far more effective in stimulating the horizontal sharing, exchange, and co-creation of tacit knowledge within the community. The administrative-dominant model, with its standardized formal interface and underdeveloped informal connections, demonstrates limited efficacy, often resulting in interrupted, one-way knowledge flow. Based on these insights, the study proposes a two-dimensional model of "institutional depth" versus "networked breadth" to describe the unique effectiveness of different governance models. Based on these empirical results, three concrete policy and management recommendations have been proposed to foster responsive rural knowledge nodes: 1) shifting performance evaluation and resource allocation from static input metrics towards a focus on dynamic "interface capability"; 2) designing and institutionalizing specialized "knowledge broker" programs to staff these interfaces with trusted, skilled intermediaries; and 3) initiating collaborative "local knowledge repository" projects to systematically capture, digitize, and valorize indigenous community wisdom. The study acknowledges limitations regarding the generalizability of findings from a three-case comparison and suggests future research directions, including longitudinal studies to observe interface evolution, social network analysis to precisely map relational structures, and exploration of how digital "smart interfaces" might integrate with the social interfaces examined here to create new paradigms for rural knowledge service.
[Purpose/Significance] Digital hoarding has emerged as a significant behavioral phenomenon in the digital age, particularly prevalent among social media users who engage in the excessive acquisition and retention of digital content. This behavior is further amplified by algorithmic recommendation systems that continuously personalize content delivery. Although existing research has examined individual psychological factors or platform characteristics using static approaches, it lacks a dynamic perspective to understand the co-evolutionary relationship between platform strategies and user behaviors. This study addresses this research gap by introducing evolutionary game theory as an innovative analytical framework. Theoretically, the significance lies in modeling the dynamic interactions between platforms' algorithmic adjustments and users' hoarding behaviors. This provides new insights into the adaptive mechanisms within socio-technical systems. From a practical standpoint, this research offers valuable implications for promoting healthier digital environments and developing sustainable governance models for platforms that balance commercial objectives with user well-being. [Method/Process] This study employs evolutionary game theory to model the dynamic interactions between social media platforms and boundedly rational users. This method is well-suited for analyzing how strategies co-evolve over time towards stable states. Based on literature from user behavior and platform economics, a game-theoretic model was developed. Numerical simulations in MATLAB analyzed evolutionary paths across four platform types (Instant Messaging, Public, Short Video, and Vertical Community), with the model calibrated against empirical typologies to investigate how key factors influence long-term outcomes. [Results/Conclusions] The simulation results reveal that the evolutionary path of the platform-user interaction system is highly sensitive to key parameters, ultimately converging to different evolutionarily stable strategies (ESS) under varying conditions. A principal finding is that a unilateral increase in algorithmic recommendation intensity by platforms, while potentially boosting short-term engagement, does not guarantee long-term benefits and may instead drive users towards non-hoarding strategies due to increased cognitive burden. Crucially, the reasonable regulation of recommendation intensity is identified as the key to achieving sustainable, positive interactions. Moderate algorithmic recommendations can effectively alleviate information overload, reduce the negative impacts of hoarding, enhance user experience and satisfaction, and ultimately increase long-term platform benefits, creating a win-win scenario. The study provides significant managerial implications, suggesting that platform operators should incorporate user well-being metrics into algorithm evaluation frameworks, moving beyond purely engagement-driven models. Differentiated governance strategies are recommended for various platform types, such as implementing intelligent filtering on instant messaging apps and content quality incentives on vertical communities. However, this study has limitations, primarily its assumption of user homogeneity, which overlooks the impact of individual differences in preferences and digital literacy. Future research should introduce user heterogeneity, explore multi-platform competition scenarios, and validate the model with empirical data to enhance its practical predictive power and application value.
[Purpose/Significance] Seoul Outdoor Library has not only gained recognition from Seoul citizens, but has also received awards from the International Federation of Library Associations and Institutions (IFLA) for two consecutive years. Since its opening, it has served 8 million users, with a user satisfaction rate of 96.6%. Moreover, it attracts the attention of the library industry both domestically and internationally. Based on this, this paper extracts replicable and scalable practical experiences and insights from the successful case of Seoul outdoor library. Its research significance lies in both addressing the dilemma of "practice taking precedence over theory" in outdoor libraries, filling the academic research gap in this field, and providing practical guidance for the long-term, high-quality development of outdoor libraries in China. [Method/Process] The research conclusions drawn from single case study methods often possess greater enlightenment and relevance to reality. Based on this, the paper analyzes the basic situation of Seoul Outdoor Library through a single case study method. Moreover, the paper adopts the "triangulation verification" multi-source data collection method to enhance the validity and reliability of the research. We found that the main service contents include book reading services, space services, art literacy education, tourism information services, and policy display and promotion services. In addition, Seoul Outdoor Library exhibits green integration and sustainability in its design, flexibility and decentralization in spatial characteristics, openness and flexibility in scene characteristics, and emphasizes interaction and human-centered service. The innovative value of Seoul Outdoor Library is reflected in the coexistence of low-cost space supply and high satisfaction, deepening the connection between libraries and public affairs, and the organic integration of social and economic benefits. [Results/Conclusions] The paper holds that the development of outdoor libraries in China should start with several aspects. Firstly, outdoor libraries should be based on observation to promote the "rediscovery of libraries" initiative. For example, outdoor libraries rediscover the new value of space, the new role of librarians, and the new connotation of resources. Secondly, outdoor libraries should be endowed with values and infused with soul, making full use of local resources to endow them with spiritual cores. Thirdly, outdoor libraries should shape their output, and optimize scene construction. Finally, outdoor libraries should nourish the heart through implementation, deeply cultivate emotional experiences, and allow users to feel a sense of belonging through humanistic details. Of course, the paper inevitably has limitations. Future research will expand case samples to gain a more comprehensive understanding of outdoor libraries and facilitate their high-quality development in China.
[Purpose/Significance] As China's low-altitude economy transitions from pilot experimentation to large-scale deployment, city governments are increasingly confronted with intelligence challenges rather than mere information shortages. Development signals are scattered across heterogeneous sources-enterprise activities, patents and R&D outputs, infrastructure readiness, investment dynamics, and municipal policy documents - often with inconsistent definitions, update cycles, and measurement units. This fragmentation raises cognitive burden and decision uncertainty: policymakers may "know a lot" but still lack a structured understanding of urban development posture, making cross-city comparison, policy-tool matching, and pathway selection difficult. To address this gap, this study re-frames index construction from an information science perspective as a data-information-knowledge transformation process and develops an interpretable measurement tool to support urban situation assessment and policy reasoning in the early diffusion stage. [Method/Process] We propose a Low-altitude Economy City Development Index (LCDI) using the analytical boundary of three heterogeneous signal systems - industrial chain, technology chain and policy chain. The index operationalizes four interpretable dimensions: technological innovation vitality, market expansion potential, ecological coordination capability, and policy empowerment effectiveness. Multiple objective data sources are integrated and normalized to ensure cross-city comparability. Indicator weights are determined through expert judgment combined with the Analytic Hierarchy Process (AHP), translating perceived importance of signals into an explicit weighting structure. The empirical assessment covers 58 Chinese cities that have issued dedicated low-altitude economy policies and satisfy data availability and comparability requirements. Beyond computing composite scores and dimension profiles, Principal Component Analysis (PCA) is used as a structural representation test: it examines whether the four-dimensional signal system can be stably abstracted into a small set of dominant cognitive axes suitable for decision-oriented interpretation. Cities are further mapped into a two-axis space and categorized via a four-quadrant configuration to facilitate type recognition and mismatch diagnosis. Finally, a concise set of typical-city cases is employed for interpretive validation, checking whether index-implied structures can be meaningfully mapped to observable governance practices and implementation pathways. [Results/Conclusions] Results reveal a clear hierarchical gradient across cities. Leading cities tend to show coordinated advantages across multiple dimensions, indicating that urban low-altitude economy development depends on systemic coupling among technology, market, ecosystem coordination, and institutional supply rather than single-factor expansion. PCA suggests that urban development posture can be summarized along two dominant structural axes: an endogenous capability axis (driven by innovation, market expansion, and coordination) and an institutional empowerment axis (driven by policy and governance capacity). The four-quadrant typology highlights structural mismatches where capability accumulation and policy supply evolve asynchronously. While the study is constrained by data availability and the sector's early-stage diffusion, the LCDI provides a replicable, updatable, and interpretable intelligence tool for cross-city comparison, type-based diagnosis, and differentiated policy calibration, and it points to future work on dynamic monitoring and broader externality indicators.
[Purpose/Significance] The global population is aging at an unprecedented pace. As a key tool to address the challenges of digital inclusiveness for the elderly, developing a digital capital scale is of utmost importance. Digital capital not only encompasses the abilities and skills of the elderly in using information technology, but also focuses on the interaction among the social resources, cultural capital, and economic capital they acquire in the digital environment. Therefore, it helps enhance the theoretical understanding of the heterogeneity of the elderly's digital capabilities. [Method/Process] First, a semi-structured interview method was adopted to conduct in-depth interviews with 24 elderly individuals based on the digital capital framework, and combined with the digital life scenarios in China. We also referred to existing studies on the digital literacy and digital capabilities of the elderly. Based on the coding results of the interview transcripts, a 7-dimensional scale for measuring the digital capital of the elderly was derived. Then, a preliminary reliability and validity analysis was conducted on a pre-test sample of 180 respondents, and the dimension indicators were appropriately adjusted. Subsequently, using the data from 380 formal questionnaires, the scale was verified and improved. Based on the principle of conceptual interpretability, the factor names of the four dimensions were re-examined, and the final version of the scale was established. Elbow estimation and the K-means clustering algorithm were then used to classify the digital capital levels of the elderly. [Results/Conclusions] The final scale consists of 19 items, covering four dimensions: digital resource acquisition ability, digital creation and expression ability, digital environment adaptation ability, and digital tool learning ability. Following optimization, the scale demonstrates excellent reliability and validity, and aligns closely with the aging-friendly scenarios. The tool can be used as a standardized tool to measure the digital capital level of the elderly population in China, laying the foundation for future large-scale surveys. By applying this scale, it is possible to effectively distinguish between groups of elderly individuals with varying levels of digital capital, providing empirical support for personalized digital services for the elderly people. For the first time, this study systematically applies the digital capital theoretical framework to the elderly population, which compensates for the lack of standardized measurement tools and highlights the unique needs and challenges of the elderly in terms of the dimensions, usage scenarios, and capability transformation. The proposed hierarchical model of digital capital among the elderly deepens our theoretical understanding of the differences in digital capabilities among this population.
[Purpose/Significance] Generative Artificial Intelligence (GAI) has rapidly reshaped the landscape of social information dissemination, bringing unprecedented network public opinion risks-such as large-scale disinformation spread, algorithmic bias-induced social inequality, extreme emotional polarization, and model hallucinations leading to cognitive deviations-that significantly amplify the complexity, suddenness, and cross-domain spillover effects of public opinion evolution. These risks not only undermine the authenticity and order of information ecosystems but also pose severe challenges to social governance, public trust, and policy-making efficiency, making accurate identification, quantitative assessment, and early warning an urgent academic and practical task. Existing research has obvious limitations: single-dimensional assessment frameworks fail to capture GAI's multi-faceted and interrelated risks, such as the concealment of generated content, algorithmic recommendation amplification and cross-platform diffusion; traditional models such as basic BP neural networks suffer from susceptibility to local optima and poor generalization, inadequately adapting to the non-linear, dynamic, and high-dimensional attributes of GAI-generated content. To address these gaps, this study constructed a 4-dimensional risk assessment index system (content, dissemination, sentiment, and user) and proposed a GA-optimized BP neural network model, which will enrich public opinion management theories in the AI era and provide practical, efficient tools for precise risk control. It will contribute to the construction of a safe, orderly, and trustworthy online space. [Method/Process] A mixed research method with solid theoretical foundations (information communication theory and intelligent optimization algorithms) and empirical support was adopted: Ten typical GAI-induced public opinion events were selected from Sina Weibo (selection criteria: views ≥1 million, original posts ≥60, covering technology, society, public affairs, and consumption fields). Following a four-stage evolutionary model (formation, outbreak, mitigation, and recovery) and four early warning levels (Level I-IV, corresponding to binary outputs 1000, 0100, 0010, 0001) as specified in national emergency management standards, samples were systematically categorized into four evolutionary stages and corresponding risk grades. A 12-indicator system covering content (authenticity, misleadingness, and professionalism), dissemination (speed, scope, and diffusion path), sentiment (intensity, polarization degree, and negative ratio), and user (influencing impact, participant activity, and interaction stickiness) dimensions was constructed. The weights of each indicator were determined to ensure objectivity, and data preprocessing was performed via min-max normalization to eliminate dimensional differences. A 4-layer BP neural network (12 input neurons, 2 hidden layers with 15 and 10 neurons respectively, and 4 output neurons) was built, with initial weights, thresholds, and hyperparameters (learning rate and iteration times) optimized by genetic algorithm (GA). A traditional BP model served as the control group, with 70% of data as the training set and 30% as the test set, and model performance was evaluated based on prediction accuracy. [Results/Conclusions] Experimental results confirm the significant superiority of the GA-BP model: its prediction accuracy reached 91.67%, 8.34 percentage points higher than the traditional BP model (83.33%). This verifies that GA optimization effectively improved model performance, enabling better capture of complex non-linear relationships among GAI-induced risk factors. The multi-dimensional index system successfully extracted core risk characteristics, realizing comprehensive identification and traceability of GAI-related public opinion risks. Limitations of this study include sample concentration on Chinese social platforms, limited case quantity, and narrow time span. Future research will expand cross-border, multi-language samples (e.g., Twitter, Facebook), enrich technical indicators (e.g., GAI content identifiability, algorithmic intervention intensity), and explore integration with deep learning models (e.g., LSTM, Transformer) to further enhance the generalizability, real-time performance, and intelligent decision-making support capabilities of the risk assessment system.
[Purpose/Significance] The rapid proliferation of generative artificial intelligence (AI), exemplified by models like DeepSeek-R1, has precipitated a paradigm shift across various sectors, positioning AI literacy as an indispensable competency for the future workforce. University students, as digital natives and pivotal agents of technological adoption and innovation, stand at the forefront of this transformation. Their proficiency in understanding, utilizing, and critically evaluating AI technologies directly influences their academic performance, research capabilities, and long-term career adaptability. Although existing literature has begun to explore the conceptual landscape of AI literacy, a significant gap remains. There is an absence of a robust, empirically validated competency framework specifically tailored to the unique learning contexts, developmental needs, and future roles of university students within China's higher education system. This study aims to address this critical gap by constructing and validating a comprehensive AI literacy competency framework for college students. Its primary significance lies in its ability to move beyond theoretical discourse and provide an evidence-based model that can guide the systematical development of targeted training programs. This enriches the theoretical underpinnings of AI literacy education and offers practical guidance for cultivating high-quality talent equipped for the intelligent era. [Method/Process] This research employed a mixed-methods approach, integrating qualitative and quantitative methods to provide both theoretical grounding and empirical robustness. The study commenced with a qualitative phase utilizing the grounded theory methodology. A systematic analysis of 112 core academic publications (2019-2024) from databases such as CNKI and Web of Science was conducted. Through a rigorous process of open coding, axial coding, and selective coding, facilitated by NVivo11 software, we extracted 300 initial concepts, which were subsequently synthesized into 26 sub-categories and ultimately 4 main categories. This process resulted in the preliminary construction of a four-dimensional AI literacy competency framework. Following this, a quantitative phase was implemented to test and refine the framework. A detailed questionnaire was developed based on the identified dimensions and indicators. Utilizing a five-point Likert scale, the questionnaire measured 26 variables corresponding to the framework's sub-components. A total of 586 valid responses were collected from undergraduate students across universities in Jiangsu Province, China. The dataset was randomly split into two halves. The first subset (N=293) underwent exploratory factor analysis (EFA) using SPSS to uncover the underlying factor structure and assess the internal consistency reliability via Cronbach's alpha. The second subset (N=293) was subjected to confirmatory factor analysis (CFA) using AMOS to verify the hypothesized factor structure, evaluate model fit indices (e.g., CMIN/DF, CFI, TLI, RMSEA), and establish convergent and discriminant validity by examining average variance extracted (AVE) and composite reliability (CR). [Results/Conclusions] The empirical analyses strongly support the validity and reliability of the proposed competency framework. The EFA clearly identified four distinct factors that aligned perfectly with the predefined dimensions, with a total variance explained of 69.916% and all factor loadings exceeding 0.6. The CFA results demonstrated excellent model fit (CMIN/DF=1.921, CFI=0.950, TLI=0.943, RMSEA=0.056), confirming the structural integrity of the framework. Furthermore, all constructs exhibited high internal consistency (Cronbach's α>0.90) and satisfactory convergent (AVE>0.5, CR>0.7) and discriminant validity. The finalized framework, therefore, comprises four interconnected core dimensions: AI Cognition (encompassing knowledge of basic concepts, applications, value, and risks), AI Skills (covering practical abilities from tool usage and programming to critical evaluation and innovation), AI Ethics (emphasizing social responsibility, privacy, intellectual property, and legal compliance), and AI Thinking (fostering higher-order cognitive abilities like computational, critical, and systemic thinking). Based on this validated framework, the study proposes a systematic and multi-faceted training system. This system outlines clear training objectives, identifies key stakeholders (e.g., university libraries, teaching centers, schools, and external enterprises), designs layered training content and pathways corresponding to each dimension, and suggests implementation strategies focusing on faculty development, a comprehensive assessment and feedback mechanism, and the strategic integration of AI-related resources. The main limitation of this study is that the respondents of the questionnaire were primarily college students during the empirical test stage. Future research can include teachers, business employers, and AI experts to modify and improve the index weight and content of the competency framework from multiple perspectives. This can be done through the Delphi method, expert interviews, and other methods, so as to enhance the framework's authority and universality.
[Purpose/Significance] Against the backdrop of national innovation-driven development strategies and the pressing need to enhance the efficiency with which scientific and technological achievements are transformed within universities, university libraries are undergoing a critical transition. They are shifting from being traditional, passive information providers to becoming proactive, embedded partners in the research and innovation value chain. However, this transition is often hampered by inherent limitations in traditional service models. This study, therefore, posits artificial intelligence (AI) as a pivotal enabler and investigates the specific mechanisms through which AI technologies can empower university libraries to achieve deep, systemic integration into the entire lifecycle of technology transfer. The research aims to provide a comprehensive theoretical framework for understanding this transformation and offer actionable, evidence-based practical pathways for academic libraries to redefine their functional boundaries and substantially strengthen the institutional support ecosystem for university technology transfer. [Method/Process] This research employs a qualitative multi-case study design, underpinned by an analytical framework constructed around the four critical, sequential stages of the technology transfer lifecycle: 1) research topic selection and project initiation, 2) research and development, 3) project conclusion and evaluation, and 4) marketization and industrialization of outcomes. Case selection followed purposive sampling criteria to ensure representation across diverse contexts, including domestic and international universities, as well as varied library types. The primary data comprised detailed case descriptions from published academic literature, institutional reports, and official service platforms. Within this staged framework, the analysis focuses on two intertwined dimensions at each phase: the evolution of the library's core service functions and the transformative impact of AI empowerment. Through a comparative cross-case analysis, this study examines how specific AI technologies augment traditional services, fundamentally changing the role and value proposition of libraries. [Results/Conclusions] The results show that through intelligent information analysis, knowledge association, data mining, and precise matching, AI can promote university libraries to shift from resource supply-oriented support to collaborative services that run through the entire lifecycle of technology transfer. This transformation manifests across the four-stage lifecycle as a shift: from providing literature to forecasting opportunities at the initiation phase; from offering patent data to navigating R&D pathways and risks during development; from archiving outputs to assessing value and potential at conclusion; and from disseminating information to intelligently brokering industry partnerships at the commercialization phase. Synthesizing these stage-specific transformations, this study constructs a novel, integrated service framework. This framework explicitly links specific AI capabilities with the redefined core functions of the library at each stage, illustrating the transition from a linear support model to a dynamic, AI-augmented ecosystem wherein the library serves as a central intelligence node. Meanwhile, this study reveals practical challenges in current practices, including ambiguous organizational boundaries, insufficient professional capabilities, and imperfect evaluation mechanisms oriented toward technology transfer. Correspondingly, it proposes strategies such as clarifying collaborative positioning, strengthening the construction of AI-empowered service capabilities, and improving technology transfer-oriented evaluation mechanisms to promote the sustainable development of AI-empowered research services in university libraries.
[Purpose/Significance] Under the strategic background of national cultural digitization and the high-quality development of public services, artificial intelligence generated content (AIGC) has become a core engine driving the digital and intelligent transformation of galleries, libraries, archives, and museums (GLAM). While AIGC offers unprecedented opportunities for content production and knowledge dissemination, current implementations often suffer from fragmentation, leading to new "data islands" and service barriers. Unlike previous studies, which treat GLAM institutions as a homogeneous whole, this paper aims to clarify the differentiated application paths of AIGC by distinguishing the unique "resource-technology-service" logic of each institution type. It seeks to reveal the structural causes of current collaborative dilemmas and construct a systematic collaborative development mechanism. This research is significant for breaking down institutional barriers, promoting the deep integration of cultural resources, and guiding GLAM institutions to shift from isolated technological upgrades to a clustered, symbiotic development model. [Method/Process] Adopting a digital ecosystem perspective, this study constructs a "Resource Attributes - Technology Adaptation - Service Goals" framework to systematically analyze the heterogeneous characteristics of the four institution types. The analysis reveals how distinct data morphologies - ranging from structured texts in libraries and semi-structured records in archives to multimodal artifacts in museums and unstructured works in art galleries - fundamentally dictate the differentiated deployment of generative text or vision models. By examining core capabilities including intelligent content twinning, editing, and creation, the study demonstrates how service goals strictly regulate technical choices: the emphasis on "access" and "trust" in libraries and archives necessitates technologies that ensure semantic accuracy and historical authenticity, whereas the pursuit of "experience" and "creativity" in museums and art galleries favors generative tools for immersive interaction and open-ended aesthetic expression. [Results/Conclusions] To address the identified challenges of fragmented development, the study proposes a tripartite collaborative development architecture consisting of a "Front-end Resource Layer," a "Mid-platform Technology Layer," and an "End-user Service Layer." The Front-end Resource Layer focuses on constructing a unified multimodal data foundation and standardized semantic ontology to bridge the semantic gap between heterogeneous institutional data. The Mid-platform Technology Layer advocates for the co-construction of an industry-specific general large model and a knowledge reasoning engine; by sharing API interfaces and computing power, this layer solves the high technical threshold and cost issues for smaller institutions, acting as a ubiquitous "industry capability hub." The End-user Service Layer aims to build a one-stop knowledge exploration portal and cross-domain expert workbenches, eliminating service isolation and creating integrated cultural scenarios. The study concludes that GLAM institutions must transition from "cultural containers" to "knowledge engines" through this architecture. Future research should further focus on copyright ethics, algorithmic governance, and new modes of human-machine collaboration to ensure the sustainable and trustworthy development of the digital cultural community.
[Purpose/Significance] This study aims to systematically examine the application of simulation modeling in bibliometrics and to clarify its methodological position within the broader framework of digital humanities tools and agricultural knowledge services. In particular, the paper highlights the innovative potential of integrating simulation modeling with generative artificial intelligence, which enables more flexible representation of heterogeneous behaviors and context-dependent decision-making processes. By bridging bibliometrics, digital humanities tools, and agricultural knowledge services, this research contributes to the theoretical advancement of bibliometric methodology and provides a structured foundation for future applications in agricultural information practice. [Method/Process] This study adopts a systematic literature-based analytical approach to review and synthesize major simulation modeling methods applied in bibliometrics. The analysis covers several representative categories of simulation models, including dynamic modeling of classical bibliometric laws, evolution models of co-authorship and citation networks, multi-agent-based simulation, information and knowledge diffusion models, and evolutionary game-theoretic models. These methods are examined with respect to their modeling objects, underlying assumptions, key parameters, and analytical capabilities. Rather than organizing the review solely by research topics, this study emphasizes simulation modeling logic as the central analytical thread. Each category of simulation method is analyzed in terms of how micro-level rules and interactions generate macro-level bibliometric patterns. Particular attention is paid to the role of digital humanities tools in operationalizing these models, especially through visualization, system integration, and interactive simulation environments that facilitate exploration and interpretation. In addition, this study introduces recent advances in generative artificial intelligence, particularly large language model-based agents, as an extension of traditional multi-agent simulation. By incorporating generative AI into simulation frameworks, it becomes possible to model heterogeneous agents with richer cognitive representations, adaptive behaviors, and contextual reasoning abilities. The methodological discussion draws on theoretical foundations from bibliometrics, complex systems, and computational social science, while also considering practical constraints related to data availability, model calibration, and validation. [Results/Conclusions] The analysis demonstrates that simulation modeling significantly enhances the explanatory power of bibliometric research by revealing dynamic mechanisms behind literature growth, collaboration structures, and knowledge diffusion processes. Compared with traditional static indicators, simulation-based approaches provide deeper insights into how bibliometric patterns emerge and evolve over time. The integration of generative artificial intelligence further expands this capability by enabling more realistic modeling of behavioral heterogeneity and context-sensitive decision-making among research actors. From an application perspective, the study shows that simulation models and associated digital humanities tools can be effectively embedded into agricultural knowledge service workflows. These applications include research evaluation, scientific information services, and policy communication, where simulation-based scenario analysis can support strategic planning and decision-making. At the same time, the study identifies several challenges, including data quality constraints, computational costs, and issues related to model interpretability and transparency. The findings suggest that future research should focus on improving data integration, enhancing model validation strategies, and further exploring the integration of generative AI to support more adaptive and explainable simulation systems. By doing so, simulation-based bibliometrics can play a more substantial role in advancing agricultural information services and research management in complex, data-intensive environments.
[Purpose/Significance] Chinese classical texts are central to preserving and transmitting traditional culture; however, promoting them among children has long faced many obstacles: the linguistic barrier posed by classical Chinese, the cognitive distance caused by cultural discontinuity, and the limitations of static and monotonous promotional forms. These challenges have often resulted in low levels of engagement and comprehension among young readers. The recent emergence of Sora-type video generation models, characterized by their ability to produce coherent long-form narratives, integrate multimodal information, and simulate spatially consistent scenes, opens up new opportunities for bridging this gap. This study aims to investigate how such models can be effectively employed in the promotion of Chinese classics among children, to evaluate their potential benefits and inherent risks, and to develop practical strategies that align technological capabilities with educational and cultural objectives. [Method/Process] This research adopts a combined approach of literature review, case study, and comparative analysis. First, it reviews existing literature on the application of artificial intelligence in reading promotion, highlighting current achievements and limitations. Second, it uses representative Chinese classics, including Shan Hai Jing, Strange Tales from a Chinese Studio (Liaozhai Zhiyi), and The Book of Songs (Shijing), to examine how Sora-generated videos function in different promotional contexts. Third, it constructs an analytical framework based on three interrelated dimensions: scenes, content, and approaches. Within this framework, the study identifies opportunities, delineates challenges, and proposes targeted countermeasures. [Results/Conclusions] Sora-type video generation can substantially enhance the promotion of Chinese classics among children. At the scene level, it allows traditional spaces to be extended into immersive and hybrid environments, thereby broadening access beyond classrooms and libraries. At the content level, it transforms abstract imagery and complex narratives into visual forms, reducing cognitive barriers and accommodating differentiated learning needs. At the approach level, it facilitates text-image complementarity, cross-media integration, and personalized recommendations, thereby strengthening engagement and sustaining reading motivation. However, the study also cautions against significant risks. These include the mismatch between generated content and specific promotional settings, the danger of oversimplification or distortion of classical texts, and the over-reliance on audiovisual materials that might undermine children's ability to engage in deep textual reading. To address these risks, the article proposes a threefold strategy: differentiated scene design, content transformation with cultural fidelity, and complementary pathways that ensure children transition from video to text.
[Purpose/Significance] Establishing and improving the mechanism for delivering public cultural services directly to the grassroots level is a key measure to promote balanced urban-rural cultural development and achieve high-quality public cultural services. [Method/Process] Based on the practical context of public cultural service construction in the new era, this study is a systematic review of the main actors and innovative practices in the Direct Delivery to the Grassroots of public cultural services. It deconstructs the core elements of establishing such a mechanism, and proposes evaluation criteria for improvement. [Results/Conclusions] 1) The study found that China's current construction of direct grassroots delivery mechanisms for public cultural services has formed a diversified model led by the government, public cultural institutions, and social forces. The government-led model, relying on strong administrative coordination and project-based operations, enables rapid and extensive service coverage. However, there are risks involved, such as prioritizing infrastructure development over sustained operations and facing challenges regarding long-term sustainability. Public cultural institutions achieve regular delivery of resources and services to grassroots levels through central-branch systems, though their effectiveness heavily depends on the central institution's coordination capabilities and the faithful implementation of institutional frameworks. The participation of social forces broadens the scope and forms of service delivery, yet the stability and public welfare orientation of their contributions are often vulnerable to market fluctuations and shifts in organizational strategies. 2) The study argues that the core of constructing a direct grassroots delivery mechanism requires three organically unified elements. First, we ensure the quality of the supplied content, that is, the resources must integrate ideological depth, popular appeal, and artistic value, in alignment with the guidance of socialist core values. Second, we must innovate the organizational methods of direct delivery channels. This can be done by exploring vertically managed central-branch systems, establishing distribution mechanisms with upward-shifted accountability, and refining systems for dispatching cultural coordinators. These changes will help overcome middle-level blockages in the resource delivery process. Finally, our goal is to achieve effective alignment between supply and demand. This involves implementing systems such as demand solicitation, menu-based distribution, and feedback evaluation, thereby shifting the service model from a government-centric approach to a citizen-centric one and fostering a virtuous cycle of precise matching. 3) The study proposes that the evaluation criteria for improving institutional mechanisms encompass four dimensions. First, service effectiveness measured by public satisfaction, social impact, and the replicability of the model. Second, cost controllability, which involves exploring sustainable operational models to reduce excessive reliance on fiscal support. Third, grassroots capacity with a focus on strengthening the ability of township (sub-district) comprehensive cultural stations to serve as hubs for resource integration and distribution. Fourth, sustainable safeguards, which entails transitioning from campaign-style investments toward institutionalized and legally supported mechanisms, reinforced by standardization and broader participation of social forces, to foster a stable and enduring developmental framework.
[Purpose/Significance] During the third plenary session of the 20th Central Committee of the Communist Party of China, the need to establish mechanisms that facilitate the direct delivery of high-quality cultural resources to the grassroots level was emphasized. This goal closely corresponds with the mission of public libraries. With the advancement of Chinese public cultural service system, public libraries have strengthened their facility networks and expanded service coverage. However, significant challenges remain, including the urban-rural disparity in cultural resources, mismatches between service supply and demand, and differences in the needs of diverse user groups. The paper addresses this research gap by reviewing innovative domestic practices, identifying their core characteristics, and clarifying pathways for mechanism development. In this way, it not only enriches the theoretical framework of Chinese public cultural service development but also provides targeted practical guidance for public libraries seeking to overcome urban-rural barriers, optimize resource allocation, and enhance grassroots access to high-quality services. The study therefore holds important theoretical and practical value for promoting the equalized, precise, and inclusive development of public cultural services and for advancing the construction of a nation of avid readers. [Method/Process] The study examines representative innovative practices from public libraries across China that promote the direct delivery of high-quality services to the grassroots level, focusing on five key dimensions: space, resources, services, activities, and professionals. Through comparative analysis, the study distills the core characteristics of these practices with respect to service goals, organizational models, implementation strategies, reform orientations, technological support, and sustainability mechanisms. Building on the analysis, it identifies priority areas for advancement and proposes targeted pathways for optimizing delivery mechanisms. [Results/Conclusions] This study found that the practice of public libraries in China in delivering high-quality services directly to grassroots level has developed a distinctive pathway characterized by six interrelated features. Firstly, it prioritizes the precise fulfillment of the diverse reading needs of different age groups and population groups as the core objective. Secondly, it relies on the central-branch library system as the organizational foundation. Thirdly, it adopts the provision of all-region and all-time reading services as the principal implementation approach. Fourthly, it regards fostering the public's intrinsic motivation for reading as an important reform orientation. Fifthly, it leverages technological innovation as a key driver for improving service efficiency. Finally, it develops reading service brands as a sustainable driving force for long-term development. To promote the sustainable and standardized development of this service model, public libraries need to establish a systematic mechanism framework across five key dimensions. Firstly, strengthening vertically integrated organizational support. Secondly, ensuring precise alignment between service supply and user demand. Thirdly, promoting comprehensive and coordinated resource supply. Fourthly, advancing multi-dimensional talent cultivation and support. Finally, establishing standardized assessment, evaluation, and long-term guarantee mechanisms.
[Purpose/Significance] The direct delivery of high-quality services by cultural centers to the grassroots level serves as a crucial pathway for fulfilling their industry mission and promoting high-quality development. It holds significant practical importance in safeguarding the fundamental cultural rights and interests of the people, improving the governance efficiency of public cultural services, and contributing to the establishment of a culturally powerful nation. [Method/Process] Based on the practical need to enhance the quality and efficiency of public cultural services, this study adopts the methods of literature analysis, case study, and practical induction. With direct service access as its core logic, the study systematically analyzes the current practical models and innovative pathways for delivering high-quality, grass-roots-level services from cultural centers. This analysis is structured across four dimensions: optimization of spatial layout, sinking allocation of resources, precise supply of activities, and support from a skilled talent team. [Results/Conclusions] The study reveals that cultural centers have established a solid foundation for coordinated planning and linkage through the central-branch cultural center system. By activating spatial efficiency, they have achieved proximity in resource allocation; by broadening service platforms, they have opened up direct access channels; through diverse and enriched activities, they have enabled universal participation and sharing; and by strengthening talent development, they have enhanced service capabilities.On this basis, to further improve service quality and efficiency, the study proposed that cultural centers should accelerate the establishment of four long-term mechanisms: an organization and coordination mechanism, a supply-demand matching mechanism, a digital empowerment mechanism, and a professional guidance mechanism. This involves continuously refining the vertical central-branch cultural center system, cultivating beloved cultural brands, and fostering a universally accessible service ecosystem. By forming a community for the development of the cultural center sector, we continuously promote the in-depth extension of high-quality public cultural services to the grassroots level. This approach will effectively transform practical and innovative achievements into a powerful driver for the high-quality development of cultural centers in the new era.
[Purpose/Significance] As an essential part of the modern public cultural service system, museums are expected not only to preserve and interpret cultural heritage, but also to respond more effectively to the growing and diverse cultural needs of the public. Against this background, this paper explores innovative practices delivering high-quality museum services directly to grassroots communities in China. The goal is to identify representative forms, distill key features, and support the development of a long-term and institutionalized mechanism for delivering museum services to these communities. This paper theoretically focuses on the structural logic and operational mechanisms of direct service delivery, thereby extending existing research on public cultural services in museums and deepening our understanding of how these services are reorganized during public cultural service transformation. In practice, this paper clarifies the major forms, common features, and developmental directions of grassroots museum services, and thus it provides useful references for improving resource allocation, optimizing service organization, strengthening grassroots service capacity, and promoting the institutionalization of high-quality museum services. [Method/Process] This paper adopts a multi-case study approach. This approach is appropriate because the direct delivery of high-quality museum services to grassroots communities is not a single, standardized process. Rather it is a complex, practical phenomenon involving multiple organizational forms, service scenarios, and the relationships between different actors. To capture such complexity, this paper selects representative cases of museum innovation in China and conducts an in-depth comparative analysis. Specifically, the analysis is organized around the four basic elements of museums, namely collections, space, technology, and people. It examines a series of innovative practices, including resource delivery, spatial direct access, network-based reach, collaborative participation, and systematic integration. By analyzing how museum collections are circulated through physical delivery and symbolic transformation, how museum service spaces are extended and embedded into grassroots settings, how digital technologies expand service reach beyond temporal and spatial constraints, how the public participates in the co-production of content and services, and how collections, spaces, technologies, and people are systematically integrated within an institutional framework, this paper reveals the internal logic and common patterns of delivering museum services directly to the grassroots communities. [Results/Conclusions] This study found that the direct delivery of high-quality museum services to grassroots communities is mainly characterized by a systematic supply, ubiquitous scenarios, precise service delivery, and collaborative participation. Based on these findings, this study proposes four pathways for further improving the mechanism of direct grassroots museum service delivery: establishing a coordinated mechanism for resource allocation, improving the operational mechanism of grassroots service facilities, optimizing the dynamic alignment mechanism between service supply and local demand, and strengthening the capacity-building mechanism for grassroots receiving entities. These findings suggest that the sustainable development of grassroots museum services depends not only on greater resource input and technological support, but also on the systematic coordination of collections, spaces, technologies, and people within an institutional framework. Due to the limited scope of the selected cases and the level of generalization, future research may examine contextual differences across museum types and regions more thoroughly. Additionally, more refined indicators may be developed to evaluate the long-term effectiveness of grassroots museum service delivery.
[Purpose/Significance] Directly channeling intangible cultural heritage services to the grassroots level is a new stage in China's progress in protecting intangible cultural heritage. This stage accurately and effectively delivers and integrates the results of intangible cultural heritage protection into the daily lives of urban and rural communities. It transforms the intangible cultural heritage resources, which were originally concentrated among minority groups and in certain areas, into accessible, perceptible, and recreatable resources for the general public. This allows intangible cultural heritage resources to truly serve and benefit the general public, and encourage innovation. The core proposition highlights the new concept of development: development by by the people, for the people, and for the people's benefit. Moreover, it is an important link in responding to the current "establishment of a mechanism for high-quality cultural resources to reach the grassroots level" in China's cultural industry. [Method/Process] Through years of practical exploration, the work of providing intangible cultural heritage services directly to the grassroots level in China has evolved from scattered projects to a systematic approach, gradually forming a practical paradigm with Chinese characteristics. This article is based on the practical application of China's intangible cultural heritage resources at the grassroots level. Through online research and on-site investigations, it provides a systematic analysis of the innovative practices and basic experiences, thus providing an empirical basis and strategic guidance for the future development of these resources. [Results/ [Conclusions] This study found that China's services for intangible cultural heritage services reaching the grassroots level rely on five models: public cultural venues, market-oriented products, large-scale festivals, national education system, and grassroots communities. After analyzing the experiences, a pluralistic "position-activity-product" system was proposed. This system links with inheritors and constructs a diversified governance pattern led by the government, driven by the market, and coordinated by social participation. Moreover, the intangible cultural heritage fusion system is deeply embedded in the national social foundation and is supplemented by digital technology, which empowers precise outreach and other experiential features. Looking towards the future, reaching the grassroots directly is not only an effective way to disseminate intangible cultural heritage, but also a strategic direction for protecting and developing intangible cultural heritage in the new era. The work of intangible cultural heritage should be based on three mechanisms: coordinating the cultivation of inheritors, activating the market, and providing public cultural support. The inheritor team mechanism should be used to build a solid foundation, the market-driven mechanism should be used to expand space, and the public cultural mechanism should be used to secure the bottom line. These three mechanisms support each other, working dynamically together to form a sustainable development mechanism that promotes the utilization of intangible cultural heritage resources at the grassroots level.
[Purpose/Significance] The delivery of public cultural services at the grassroots level is a key issue in developing China's modern public cultural service system. Although digital technology has created new possibilities for wider access to cultural resources, extending high-quality public cultural services to county, township, and village communities remains challenging. Problems such as uneven resource distribution, weak supply-demand coordination, and limited grassroots service capacity continue to affect service effectiveness. In the context of the national cultural digitization strategy, public digital cultural services have emerged as a key means of enhancing grassroots service delivery. Existing studies have discussed policy development, system construction, platform building, service evaluation, and digital inclusion, but relatively less attention has been paid to how public digital cultural services actually function in the process of reaching the grassroots level. This article focuses on this issue and examines the practical steps, internal logic, and realistic constraints involved in extending public digital cultural services to the grassroots. [Method/Process] This article combines case analysis and policy text analysis. It takes the National Public Culture Cloud as the central case and draws on relevant practices in Shanghai, Guangzhou, Zhejiang, and other areas in China. This research design is appropriate because the development of public digital cultural services is influenced not only by technological conditions, but also by policy guidance, institutional arrangements, and local practices. Theoretically, the article draws on public service provision and technological empowerment theories. On this basis, it develops a three-dimensional analytical framework consisting of platform architecture, operating mechanisms, and service scenarios. Through this framework, the article examines how digital platforms support the transmission of cultural resources, how service effectiveness is improved through demand matching, social participation, and user cultivation, and how mobile, immersive, and intelligent applications reshape grassroots cultural participation and user experience. [Results/Conclusions] The study shows that public digital cultural services have paved the way for the expansion of public cultural resources to the grassroots level. However, their effectiveness is not solely dependent on technology. A multi-level platform system has provided technical support for resource connection and transmission, yet inconsistent standards, insufficient data sharing, and repeated platform construction still reduce overall efficiency. At the same time, demand matching, social participation, and user cultivation are important for improving service quality and strengthening grassroots participation. Mobile, immersive, and intelligent service scenarios are also changing the way users access and experience public cultural services. However, digital divide, weak local operation capacity, and insufficient data governance remain major constraints. Therefore, the development of public digital cultural services should move beyond platform building and resource aggregation, and pay greater attention to standard coordination, mechanism improvement, service innovation, and inclusive access. Since this study is mainly based on policy materials and typical cases, future research should focus on strengthening field investigations and comparative analyses.
[Purpose/Significance] New rural public cultural spaces represents an innovative approach to advancing rural revitalization and the high-quality development of public cultural services in the new era. They also serve as a key vehicle for promoting the integrated development of urban and rural public cultural services. This study aims to systematically analyze the development models and operational logic of new rural public cultural spaces and to explore pathways for their high-quality advancement. [Method/Process] Based on field investigations and interviews conducted between September 2024 and March 2025 at more than 40 new rural public cultural spaces in Beijing, Shandong, Zhejiang and other regions, this study employs case induction and comparative analysis to systematically examine their major types, construction and operational models, and development pathways, and to distill their common operational mechanisms. [Results/Conclusions] The findings indicate that new rural public cultural spaces encompass diverse types, including public reading spaces, art promotion spaces, local cultural exhibition spaces, culture-tourism integration spaces and digital cultural experience spaces. In terms of construction and operational models, three main governance structures have emerged: government-led construction with diversified operation, society-led construction and operation, and multi-stakeholder collaborative co-construction and co-management. These models exhibit significant differences in the allocation of responsibilities and rights, resource distribution and operational approaches. Regarding development pathways, due to variations in resource endowments, development motivations and target service groups, new rural public cultural spaces demonstrate diversified development patterns, including local culture-based, industry-driven, scenic-area-embedded, eco-integrated and community-oriented models. Despite differences in construction models and development pathways, new rural public cultural spaces have gradually formed several common operational mechanisms in practice, mainly including embedded operation, localized integration, activity-driven development, multi-stakeholder collaboration, emotional narrative construction and light-asset operation. The pathways for promoting the high-quality development of new rural public cultural spaces during the 15th Five-Year Plan period mainly include the following: 1) Optimizing spatial layout. This involves strengthening the integration and functional coupling of existing public cultural facilities, promoting the cultural regeneration of idle rural buildings, and building a symbiotic matrix linking cultural spaces with cultural and tourism industry nodes, thereby embedding new rural public cultural spaces more effectively into the overall rural development system. 2) Enhancing content provision. By deeply exploring local cultural resources, distinctive cultural brands with strong local identity can be developed. At the same time, professional teams should be introduced to design themed cultural activities, forming a content ecosystem that integrates local cultural resources with modern creativity. 3) Promoting differentiated development. Functional configurations should be determined according to local cultural resource endowments and actual service needs, encouraging new rural public cultural spaces to develop differentiated positioning and complementary functions. 4) Advancing socialized operation. By further developing the "space curator" mechanism for rural cultural spaces and establishing an institutional support system aligned with socialized operation, the vitality of social participation can be effectively stimulated.
[Purpose/Significance] University libraries require efficient, data-driven academic evaluation to support management decisions. Traditional manual methods are slow, subjective, and untimely. While large language models (LLMs) offer automation potential, existing applications in this domain are limited, often focusing on auxiliary tasks and raising data security concerns with cloud-based processing. This study addresses these gaps by proposing a localized, intelligent agent for secure and interactive analysis of academic output. [Method/Process] A four-layer theoretical framework based on the DIKW model was established to guide the agent's design from data integration to wisdom generation. Grounded on the practical experience of academic evaluation services in libraries, this study systematically identified data requirements from dimensions of academic evaluation objects (institution, school, discipline, and researcher) and metrics (output, collaboration, impact, and quality), and formulated a metadata scheme to integrate bibliographic data, indexing data and evaluation data into a single structured table for research papers. A localized agent was implemented using open-source tools: Chainlit for the conversational interface, LangChain with the Kimi-K2-0905-Preview LLM as the core, and the ReAct framework to enable an iterative "Thought-Action-Observation" loop for complex reasoning and self-correction. The agent employs Text-to-SQL technology to translate natural language queries into executable PostgreSQL statements. Comprehensive prompt engineering was conducted to guide the LLM in accurate SQL generation, handling challenges such as data deduplication, multi-value fields, and entity disambiguation. This enables dynamic intent interpretation, multi-step data retrieval and validation, and output generation combining visualizations and structured reports. [Results/Conclusions] The agent was evaluated using a test dataset of over 30 000 structured academic papers and a multi-dimensional set of 20 test queries covering various evaluation scenarios and complex composite questions. The agent achieved a 100% final accuracy rate. The initial query accuracy was 85%, with errors primarily related to recognizing informal entity names (e.g., abbreviations). All errors were autonomously corrected within one ReAct iteration, demonstrating effective self-repair. Comparative analysis against two general-purpose data analysis agents showed the proposed agent's superior accuracy and stability, particularly in handling entity disambiguation and complex multi-turn tasks. The study confirms that the locally-deployed intelligent agent provides an effective, secure, and interactive solution for academic output analysis, successfully bridging natural language queries with precise data retrieval. Limitations include the evaluation's primary focus on data retrieval accuracy rather than narrative quality, and a test scope limited to core academic evaluation queries. Future work will expand the agent's capabilities to support diverse research outputs (e.g., patents and monographs), enhance visualization integration, and enable customizable report template generation.
[Purpose/Significance] Unleashing the consumption potential of rural residents plays a pivotal role in expanding domestic demand and cultivating new economic growth points. The digital economy, driven by data elements as the core force, is gradually becoming a key engine to activate the consumption potential of urban and rural areas in China and promote consumption upgrading. National Big Data Comprehensive Pilot Zones (NBDCPZs), with their "agglomeration of data elements, cross-domain collaborative empowerment, and precise service matching", continuously meet the personalized and diversified consumption demands of rural residents, and have unique value in unleashing the consumption potential of rural residents. [Method/Process] After conducting a theoretical analysis of the impact of the NBDCPZs on the consumption potential of rural residents, this study formulates corresponding research hypotheses. This study uses data from the China Family Panel Studies (CFPS) from 2010 to 2022 and considers the "National Big Data Comprehensive Pilot Zones" policy as a quasi-natural experiment. On the basis of measuring rural residents' consumption potential using the propensity score matching (PSM) method, the difference-in-differences (DID) method is employed to evaluate the impact of NBDCPZs construction on rural residents' consumption potential. [Results/Conclusions] The research findings are as follows: 1) After balancing the endowment characteristics of urban and rural households via the PSM method, the per capita consumption expenditure of rural residents was found to be 2 255.23 yuan less than that of urban residents. This indicates that rural areas still have enormous untapped consumption potential. 2) The construction of NBDCPZs significantly promotes the release of rural residents' consumption potential, and this conclusion remains robust after undergoing the parallel trend test, placebo test, counterfactual test, addition of fixed effects, and exclusion of the impacts of other policies. 3) An analysis of heterogeneity across sample household and regional characteristics reveals that the effect of NBDCPZs construction on unlocking rural residents' consumption potential is particularly prominent in eastern China, and is more salient in rural households with a male household head, low income, and middle-aged composition. 4)The mechanism of action indicates that the "National Big Data Comprehensive Pilot Zones" policy releases the consumption potential of rural residents by increasing their income levels and enhancing technological progress in rural areas. Furthermore, household debt exerts a positive moderating effect on the process of releasing rural residents' consumption potential through the construction of the National Big Data Comprehensive Pilot Zones. Based on the research conclusions, the following countermeasures and suggestions are put forward: 1) Advance the differentiated layout and integrated application of rural digital infrastructure; 2) Establish a long-term mechanism for enhancing rural residents' digital literacy; 3) Optimize the income increase system for rural residents and consolidate the foundation for consumption upgrading.
[Purpose/Significance] Agents driven by large language models (LLMs) are transitioning from executing tasks based on predefined workflows to autonomously planning and taking dynamic actions in complex environments. Thus, scientific and technical literature intelligence work is expanding its scope from stage-based information processing to continuous intelligence analysis. OpenClaw is an open agent execution framework that integrates capabilities such as multi-channel access, task orchestration, tool invocation, memory maintenance, and persistent operation. However, a systematic examination of its potential applications, impacts, and practical constraints in the field of scientific and technical literature intelligence remains lacking. This study aims to analyze OpenClaw's technical architecture and comprehensively evaluate how it transforms literature retrieval, knowledge organization, intelligence analysis, and knowledge service processes. [Method/Process] This study systematically reviewed the technical architecture and core components of OpenClaw, focusing on four interdependent layers: the access and communication layer, the agentic loop execution layer, the tool invocation and capability extension layer, and the memory maintenance layer with persistent operation. It related the OpenClaw's architectural logic to the organization of scientific literature, intelligence analysis workflows, and knowledge service scenarios. The study further discussed the changes that OpenClaw's architectural logic may bring to scientific and technical literature intelligence work, including shifts in retrieval patterns, knowledge processing methods, analytical workflows, and service outcomes, as well as the technical and ethical challenges that may emerge in practice. [Results/Conclusions] OpenClaw provides a new technical reference that transitions scientific and technical literature intelligence work from stage-based information processing to continuous knowledge work. Nevertheless, critical challenges persist. These include planning reliability, adaptability of domain knowledge, stability of tool integration, interpretability and traceability, and ethical risks related to data privacy, accountability, and open-ended extension mechanisms. To address these challenges and support the rapid development of an intelligent scientific and technical literature intelligence system, this study proposes the following seven interrelated development directions: 1) establishing task inventories for high-value scientific and technical intelligence scenarios, 2) developing a hybrid foundational model paradigm that combines general-purpose LLMs, task-specific large models, small models, and specialized tool sets, 3) advancing a one-click service model for autonomous scientific discovery and intelligent intelligence support, 4) constructing national-level corpora of literature and intelligence resources tailored to intelligent scientific and technical intelligence scenarios, 5) promoting in-depth collaboration between large, domain-specific models and agents, 6) designing a safe and controllable roadmap for progressive deployment, and 7) establishing a full-process, closed-loop governance mechanism. These proposals are expected to provide valuable references for both disciplinary development and professional practice. They will facilitate the gradual transformation of agents from auxiliary tools into a vital capability for the future of scientific and technical literature intelligence work.
[Purpose/Significance] The rapid evolution of artificial intelligence technologies from dialogue-based generation to autonomous task execution marks a paradigm shift with profound implications for library services. A new generation of AI agents, exemplified by the open-source project OpenClaw, can independently plan multi-step tasks, invoke external tools, operate computer interfaces through visual perception, and deliver structured work products with minimal human intervention. The shift from "answering questions" to "completing tasks" fundamentally challenges the traditional library service model. The model has long been based on the idea that librarians serve as the primary connection between information resources and users. Libraries worldwide are facing an increasing structural tension: their collections are expanding while their staffing levels are remaining constrained, resulting in unmet knowledge service demands. Agent technologies, with their capabilities for autonomous planning, tool invocation, environmental perception, and persistent memory, offer a potential pathway to address this gap. However, the library community currently lacks a systematic analytical framework through which to understand how this technology paradigm intersects with existing service architectures, governance requirements, and organizational structures. This study addresses this gap by providing both a conceptual framework for analyzing agent technologies in the library context and practical guidance for their implementation and governance, contributing to the broader discourse on intelligent library transformation as articulated in national science and technology development strategies. [Method/Process] This study employs a multi-method research design with OpenClaw as the primary analytical lens. The technical architecture analysis involves systematic examination of OpenClaw's publicly available documentation, GitHub source code repository, and official technical publications. Four core mechanisms are deconstructed in detail: the Computer Use Agent paradigm, which enables vision-driven interface operation through periodic screen capture, multimodal language model interpretation, and simulated mouse and keyboard actions; the local-first architecture with model-agnostic design, which maintains data sovereignty through a decentralized gateway-node topology while supporting flexible switching among multiple large language models; the Heartbeat mechanism, which transforms the agent from a passive responder into a proactive monitor through a condition-triggered self-inspection cycles; and the Model Context Protocol, an open standard for tool integration that enables any MCP-compliant agent to invoke standardized service capabilities. Case comparison analysis evaluates two contrasting platform approaches for supporting agent deployment in libraries - FOLIO Eureka, representing the next-generation Library Service Platform pathway with its microservice architecture, API gateway, and event-driven communication, and the Cloud Alliance's A-LSP, representing an agent-native design philosophy that positions intelligent agents as the core organizational principle of library service platforms. Policy document analysis examines the IFLA Guide on the Introduction of AI in Libraries, China's New Generation Artificial Intelligence Development Plan, the Data Security Law, and the Personal Information Protection Law, as well as regional policy experiments in agent technology promotion. Security incident case studies draw from the ClawHavoc supply chain attack, which compromised over 21 000 active instances; Cisco Talos security audits, which revealed prompt injection vulnerabilities; and CrowdStrike threat assessments, which identified misconfiguration risks that could transform agents into attack vectors. [Results/Conclusions] The study proposed a critical distinction between "narrow OpenClaw" (the specific open-source product and its derivative ecosystem) and "broad OpenClaw" (the agent technology paradigm it represents), arguing that libraries must engage strategically with both dimensions while avoiding the twin pitfalls of conflating technology trends with product procurement decisions or dismissing an entire paradigm based on the limitations of a single product. The narrow application analysis identified three viable deployment scenarios - personal productivity tools for librarians, information collection and subject monitoring, and reader-facing service prototyping - while documenting associated risks in technical stability, supply chain security, and regulatory compliance. The broad paradigm analysis revealed five structural impacts on libraries: diversification of service entry points through embedded integration, transformation from reactive response to proactive push services, evolution of reader information behaviors from search to delegation, disruption of commercial ecosystems including usage-based pricing models, and fundamental repositioning of libraries as knowledge infrastructure in the AI ecosystem. Four architectural prerequisites for agent deployment were identified: API openness, event-driven capabilities, permission governance, and observability, with insufficient system openness identified as the primary bottleneck that constrains implementation. Three differentiated implementation pathways were proposed with corresponding phased strategies. A comprehensive governance framework has been constructed encompassing six dimensions: system security with defense-in-depth measures, data governance and privacy protection aligned with national legislation, ethical standards addressing algorithmic bias and hallucination risks, copyright compliance addressing the ambiguity of agent-mediated access under existing licensing agreements, human-agent collaboration through a tiered oversight system, and standardization initiatives including library-specific MCP tool standards. The study also proposed institutional innovations such as "agent sandbox zones" that allow controlled experimentation in isolated environments. The research concluded that the highly structured and process-oriented nature of library workflows makes libraries a particularly suitable domain for agent technology adoption, but successful implementation depends on the coordinated advancement of technical readiness, governance maturity, and organizational change capacity. Limitations of this study include the nascent stage of actual agent deployment in libraries, which means the proposed frameworks await empirical validation. Future research directions include conducting empirical studies of library agent deployments, developing standardization pathways for cross-library agent collaboration, investigating copyright licensing adaptation mechanisms for agent-mediated access, and examining the long-term impact of agent technologies on the library profession and library science education.
[Purpose/Significance] With the rapid advancement of generative artificial intelligence, open-source agents have emerged as a key driving force in reshaping the development paradigm of artificial intelligence. These agents integrate foundation models, tool chains, and collaborative mechanisms to enable autonomous task execution. From the perspective of technological evolution, this paper conducts a systematic analysis of three core issues concerning open-source agents: technological evolution, application scenarios, and security governance, aiming to clarify the evolutionary laws of open-source agents, expand their application boundaries, and provide theoretical and practical references for their standardized development and rational application. [Method/Process] In terms of evolutionary stages, this study proposed that open-source agents have undergone four distinct phases: pre-history of technology (primitive tool integration stage), single-point intelligence (independent task execution stage), systemic intelligence (multi-tool collaborative stage), and ecological intelligence (multi-agent symbiotic stage). This evolution was driven by a shift in focus from relying solely on the capabilities of foundation models to gradually forming a mature agent ecosystem featuring autonomous operation, multi-agent collaboration mechanisms, and cross-platform interoperability. As a prime example of a practice in the ecological intelligence stage, OpenClaw, with its open-source architecture, modular design, and multi-agent collaborative capabilities, marked a paradigm shift in artificial intelligence, moving from "Model as a Service" (MaaS) to "Agent as a Service" (AaaS) and enabling end-to-end task closed-loop management. Regarding application dimensions, a three-tier progressive scenario framework was constructed, encompassing knowledge-intensive assistance (such as academic research and intelligent consulting), tool-intensive execution (such as automated office and industrial control), and collaboration-intensive processes (such as public governance and team collaboration), emphasizing that its core value lies in accomplishing complex end-to-end tasks in a verifiable, auditable, and sustainable manner. In the governance dimension, a four-layer embedded governance framework was proposed to address the security risks and regulatory challenges posed by open-source agents. This framework covers the protocol layer (standard formulation), the platform layer (technical supervision), the execution layer (behavioral constraints), and the ecological layer (industry self-discipline). [Results/Conclusions] This study found that the competitive focus of open-source agents has gradually shifted from the performance of individual models to ecological controllability and governance credibility. As a new type of intelligent entity, whether open-source agents can effectively support diverse application scenarios, such as scientific research, public governance, and industrial production, fundamentally depends on their ability to realize the deep integration of advanced technological capabilities and sound institutional trust. This also provides a core direction for the future development and governance of open-source agents.
[Purpose/Significance] The use of large language models (LLMs) for patent text mining has become a major research topic in recent years. However, existing studies mainly focus on the application of LLMs to specific tasks, and there is a lack of systematic evaluation of the application effects of fine-tuned LLMs across multiple scenarios. To address this problem, this study takes ChatGLM, an open-source LLM that supports local fine-tuning, as an example. We conduct a comparative evaluation of three types of patent text mining tasks-technical term extraction, patent text generation, and automatic patent classification-under a unified experimental framework. The performance of fine-tuned models is compared from six aspects: different training data sizes, different numbers of training epochs, different prompts, different prefix lengths, different datasets, and single-task versus multi-task fine-tuning. [Method/Process] This study was based on an open-source LLM and carried out fine-tuning research for specific patent tasks in order to clarify the impact of different fine-tuning strategies on the performance of LLMs in patent tasks. Considering task adaptability, model size, inference efficiency, and resource consumption, ChatGLM-6B-int4 was selected as the base model, and P-Tuning V2 was adopted as the fine-tuning method. Three categories of patent tasks are included: extraction, generation, and classification. The extraction task is patent keyword extraction. The generation tasks include: 1) innovation point generation; 2) abstract generation based on a given title; 3) rewriting an existing title; 4) rewriting an existing abstract; 5) generating novelty points based on an existing abstract; 6) generating patent advantages based on an existing abstract; and 7) generating patent application scenarios based on an existing abstract. Six experimental comparison dimensions are designed: 1) different training data sizes; 2) different numbers of training epochs; 3) different datasets with the same data size; 4) different prompts under the same task and data; 5) different P-Tuning V2 prefix lengths with the same training data; and 6) single-task fine-tuning versus multi-task fine-tuning. Two type of evaluation metrics were used. For extraction and generation tasks, the BLEU metric based on n-gram string matching was adopted. For classification tasks, accuracy, recall, and F1 score were used. [Results/Conclusions] Based on the fine-tuning results, several conclusions were obtained. First, a larger training data size does not always lead to better performance. Second, the appropriate number of training epochs depends on the data size. Third, under the same data distribution, different data subsets have limited influence on performance. Fourth, under the same task and dataset, different prompts have little impact on model performance. Fifth, the optimal prefix length is closely related to the training data size. Sixth, for a specific task, single-task fine-tuning performs better than multi-task fine-tuning. These conclusions provide reference and guidance for fine-tuning LLMs in practical patent information work.
[Purpose/Significance] As the user base grows, the number of online comments is increasing rapidly. The massive volume of comments has broadened the innovative thinking of enterprises and provided more diverse innovative options, but it has also brought about the problem of information overload. Therefore, in the face of the massive amount of online user comments, how to use efficient and precise methods to mine information with practical value, effectively integrate valuable information and identify product innovation opportunities, and transform it into high-quality resources for enterprise product innovation has become a hot topic of great concern in both academic and industrial circles. Against this backdrop, studying how to identify product innovation opportunities based on online reviews is of great theoretical significance and practical value. Unlike previous studies, this paper uses the BERT model to accurately filter out negative user comments and identify key demand points. This article also combines the characteristics of ordinary users and leading users, integrates dual-source data of user comments from e-commerce platforms and online communities, and associates the demand issues of ordinary users with the suggestions of leading users, which can more accurately identify product innovation opportunities. [Method/Process] First, we collected and pre-processed ordinary user comment data and leading user comment data. Second, the BERT model and LDA topic model were used to categorize the sentiment and cluster the comment data to mine the problems of ordinary users and suggestions of leading users. Finally, based on semantic similarity analysis, problem-suggestion topic mapping was realized to identify product innovation opportunities with high innovation value. [Results/Conclusions] This paper constructed a problem-suggestion product innovation opportunity identification method driven by dual-source data, and selected the action camera as a case to elaborate in detail on the specific practice of the proposed method in the field of product innovation. Through case analysis, the feasibility of the proposed method of product innovation was verified, providing an operational reference basis for enterprises on how to efficiently recommend product innovation work. However, this paper still has certain limitations and needs to be improved with more abundant data in subsequent studies. First, the data collected in this article mainly come from e-commerce platforms and online community platforms. Although this data contain a large amount of user information, there are still deficiencies. In the future, we will introduce more data sources, such as news media and technology websites to obtain more comprehensive and diverse data. Second, this paper has only conducted case application research in the field of intelligent digital products. In the future, we need to further explore more fields, such as smart wearables and whole-house intelligence, to enhance the universality of the product innovation opportunity identification framework constructed in this paper.
[Purpose/Significance] The rapid advancement of generative artificial intelligence (AI) is driving societal digital transformation, yet it simultaneously poses unprecedented systemic risks to personal information security due to the large-scale, automated, and complex nature of its data processing. Previous research has lacked exploration of governance pathways that consider endogenous technological evolution and specific model iterations. This paper takes the technological evolution of mainstream, large-scale generative AI models, both domestically and internationally as a starting point, and systematically reveals the impact of generative AI on personal information protection principles across the stages of data collection, model operation, and content generation. The focus is on analyzing how technological innovations in China's DeepSeek, including open-source traceability, decision transparency, and flexible deployment, lay the groundwork for risk-graded governance. This study not only broadens the theoretical perspective on AI governance and promotes the formation of a "technology-institution" collaborative governance paradigm, but also offers innovative and actionable insights for building an agile and effective personal information protection system in China amidst the rapid adoption of generative AI. [Method/Process] This study employs a comparative analysis and inductive research approach. First, it systematically compares the core technological differences among mainstream generative AI models, both domestic and international, across three dimensions: model ecosystem, model capabilities, and deployment methods. Through this comparison, it analyzes the challenges generative AI poses to personal information protection at various stages, including data collection, model operation, and content generation. Second, the study systematically examines the differentiated impacts brought about by DeepSeek's technological iterations on personal information security governance. Building on this foundation, the research proposes a comprehensive governance strategy centered on the principles of inclusiveness and prudence, guided by risk grading, and covering all operational stages of generative AI. This strategy emphasizes the critical role of DeepSeek's technical characteristics in supporting the implementation of this framework. [Results/Conclusions] The research indicates that constructing a risk-graded governance system based on the sensitivity of personal information is an effective approach to balancing security and innovation in generative AI. This system emphasizes distinguishing between sensitive and general information during data collection, achieving traceability and purpose control during model operation, and implementing differentiated security safeguards during content generation. With its technical advantages, including open-source traceability, decision transparency, and flexible deployment, DeepSeek provides technical validation and practical possibilities for graded governance. This facilitates the protection of sensitive personal information in high-risk scenarios while simultaneously fostering technological iteration and application innovation in medium- to low-risk contexts. Future research should further incorporate multi-dimensional governance elements such as industry self-regulation, social coordination, and international collaboration. Empirical analysis should also be conducted to test the applicability and effectiveness of the governance framework, thereby gradually developing a well-rounded personal information security governance scheme that adapts to the dynamic evolution of technology.
[Purpose/Significance] The accelerated digital transformation of public cultural services has fundamentally reshaped modes of service delivery, governance frameworks, and citizen engagement. Exploring how digital technologies empower the high-quality development of public cultural services is essential for designing a modern, equitable, and efficient service system. This study contributes to the existing literature by systematically investigating not only the direct effects of digital technologies but also threshold, regional heterogeneity, spatial spillovers, and mediating mechanisms. This clarifies how digital innovation interacts with governance capacity and institutional environments. Unlike previous research, which relied mainly on descriptive or single-method analyses, this study employs an integrated empirical framework. This framework captures the dynamic and multidimensional nature of digital empowerment within the context of public service. It enriches the theoretical and practical understanding of digital governance. [Method/Process] This study employs panel data from 31 Chinese provinces over the period 2015-2023 to systematically investigate how digital technologies influence the high-quality development of public cultural services. A combination of fixed-effects models, mediating-effects models, threshold regression models, and spatial econometric models was used to capture direct, nonlinear, regional, spatial, and mediating effects. To control for potential confounding factors, fiscal expenditure, population density, and cultural literacy were incorporated as covariates. The analysis drew on theoretical foundations conceptualizing digital technology as a new productive force and was supported by empirical data from national statistical yearbooks, digital finance indices, and governance performance indicators, ensuring both methodological rigor and contextual relevance. [Results/Conclusions] Digital technology significantly promotes the high-quality development of public cultural services, with measurable positive effects for each incremental increase in the digital technology development index. The influence exhibits a nonlinear threshold pattern, reflecting a "promotion-weakening-enhancement" trajectory, highlighting the necessity of integrating technological applications with governance structures, resource allocation, service design, and public digital literacy. Regional analyses reveal stronger effects in the central and western provinces, suggesting that digital technologies can help mitigate service disparities under supportive policy frameworks. The spatial econometric results indicate positive spillover effects on neighboring regions, while the mediation analysis identifies government governance capacity as a key mechanism through which technological inputs translate into service outcomes. Policy implications include reinforcing digital infrastructure, enhancing institutional support, implementing region-specific strategies, fostering inter-provincial coordination, and strengthening government-led service integration. The study has limitations, including the possibility of potential unobserved concurrent causal pathways, Future research should adopt configurational methods such as qualitative comparative analysis in future research to further elucidate the complex, multicausal dynamics of digital technology empowerment in public cultural services.
[Purpose/Significance] With the rapid integration of generative artificial intelligence into library services, user trust in information has begun to exhibit a new pattern characterized by high usage, low certainty, and increased reliance on institutional guarantees. Existing studies on online credibility, artificial intelligence generated content (AIGC) applications and library innovation have mostly examined either technical performance, information literacy, or governance issues in isolation. Few have systematically analyzed how specific AIGC features, user capabilities and the institutional environment of libraries jointly shape multi dimensional user trust. This study focuses on AIGC supported services in public and academic libraries and constructs a comprehensive analytical framework linking technological signals, user ability and library based institutional mediation to the formation of cognitive, emotional and behavioral trust. The paper contributes to the refinement of trust theory in digital information environments by providing empirical evidence from a large-scale sample in China. It also offers actionable insights for libraries seeking to deploy AIGC while maintaining or enhancing their role as trusted public knowledge institutions. [Method/Process] The study is grounded in classic research on cognitive authority and online credibility, and combined with recent work on AIGC, knowledge services, information literacy and library governance. It conceptualizes user trust as a three dimensional construct comprising cognitive, emotional and behavioral components. AIGC related technological features are operationalized along three axes: perceived content quality, generation transparency and interactivity. User capability is measured through standardized digital literacy tests and indicators of professional background, while the library environment is captured by the presence of institutional arrangements such as usage guidelines, staff verification, result labelling and risk reminders. Data were collected through a large-scale questionnaire survey in ten public and academic libraries in Henan Province, yielding 2 347 valid responses. After data cleaning and reliability and validity checks, the study employed a combination of structural equation modelling, two stage least squares estimation, threshold regression, spatial autoregressive models, dynamic panel system GMM estimation, quantile regression and finite mixture models. This sequential strategy allowed for simultaneous identification of structural paths, endogenous relationships, non linear and moderating effects, spatial spillovers and temporal dependence, as well as heterogeneous trust formation patterns across user groups. [Results/Conclusions] The findings confirm that user trust in AIGC enabled library services is best understood as a three dimensional structure, in which cognitive trust influences emotional trust, and both jointly shape behavioral trust. Content quality and generation transparency exert strong and robust positive effects on cognitive trust, while interactivity mainly enhances emotional trust and indirectly affects behavioral intentions. Digital literacy and professional background introduce clear threshold and amplification effects: when user capability is below certain levels, improvements in content quality and transparency have limited impact on trust, but above these thresholds the marginal effects increase markedly. Library level institutional arrangements, including human review, explicit labelling and standardized usage rules, not only raise overall trust levels, but also significantly strengthen the effects of technological signals, sometimes to a degree comparable with individual level capability factors. Spatial and dynamic analyses show that trust exhibits both spillover and path dependence: practices in one library can influence neighbouring institutions through user mobility and word of mouth, and positive or negative experiences accumulate into longer term evaluations. The study suggests that libraries should treat trust building as a core design objective when introducing AIGC, embed transparency and quality signals into interfaces and metadata, establish robust verification and correction workflows, and provide differentiated services for users with different literacy levels and professional backgrounds. The limitations include the concentration of data in one province and the use of primarily macro-level instruments for identifying causation. Future research could extend the framework to cross regional and cross type libraries, compare specific functional scenarios such as reference services and reading promotion, and further integrate trust analysis with broader issues of library governance, literacy education and responsibility allocation in AIGC ecosystems.
[Purpose/Significance] This study investigates the operational practices and strategic development pathways of intelligent consultation services in libraries globally, specifically under the impetus of artificial intelligence (AI) large language models (LLMs). By conducting a systematic analysis of representative case studies, we examine the applied technologies, emerging service models, and measurable efficacy of these AI-enhanced services. The research holds significance in offering actionable insights for the effective implementation of AI within the library sector. It aims to guide the evolution of intelligent consultation toward greater innovation and cultural-contextual adaptability, thereby providing both theoretical underpinning and practical guidance for the localized development of smart library ecosystems. [Method/Process] Employing a comparative case study methodology, this research selected 30 representative libraries from diverse international and domestic contexts as its subjects. Data were primarily gathered through structured online surveys and content analysis of publicly available service interfaces, systematically capturing the scope, functionality, and operational status of their intelligent consultation services. The analysis focused on characterizing technological applications-identifying core LLM integrations, typical functionalities, and architectural highlights. It further integrated findings to compare and contrast prevailing service models and implementation variances. Subsequently, the study conducted a multidimensional comparative assessment of the practical service effectiveness enabled by AI large models, evaluating performance across four key areas: service response efficiency and accuracy; capabilities in resource organization and structured knowledge management; tangible improvements in user service experience; and degree of service model innovation. [Results/Conclusions] The findings indicate that AI large model-driven intelligent consulting services exhibit pronounced advantages in key operational metrics, including enhanced response efficiency, superior knowledge synthesis and management capabilities, enriched user interaction experiences, and the facilitation of novel service paradigms. However, a comparative analysis reveals significant disparities among libraries concerning the depth of technological integration, the sophistication of service offerings, and the level of cultural and linguistic adaptation achieved. In response, the study proposes targeted strategic recommendations from three interrelated perspectives: nuanced technological application, user-centered service design, and collaborative ecosystem construction. It advocates for libraries to prioritize the synergistic balance between technological capability and humanistic service values, to achieve deeper integration with localized and institutional knowledge repositories, and to institute mechanisms for continuous service evaluation and iterative optimization. These approaches are essential for fostering more efficient, inclusive, and sustainable development of intelligent consultation services. Future research directions should encompass longitudinal studies on service effectiveness, the integration of multimodal interactive capabilities, and the formulation of ethical guidelines and governance frameworks for AI deployment in library services.
[Purpose/Significance] This paper traces the historical formation and intellectual lineage of Chinese bibliography, extracting its core iconic concepts and the knowledge system they constitute. By analyzing the opportunities and challenges confronting the discipline in the contemporary era, this study aims to provide theoretical support for constructing an autonomous knowledge system of Chinese bibliography that is both self-sustaining in its Chinese character and open to global academic exchange. This research is significant because it attempts to reposition traditional Chinese bibliographic scholarship within the modern academic landscape. It does this by bridging classical heritage with contemporary information science, and by responding to the urgent need for a distinctively Chinese theoretical voice in the global humanities. [Method/Process] Through systematic synthesis and conceptual analysis, this study identified and examined a set of foundational iconic concepts in Chinese bibliography, including jiaochou (collation and textual criticism), qiushu (methods of seeking and recovering lost texts), jieti (explanatory titles and interpretive annotations), zhulu (cataloging and bibliographic recording), zhushi (commentary and exegesis), fenlei (classification systems and modalities), and leixu (categorical prefaces and taxonomic introductions). These concepts collectively form a distinct knowledge system that has evolved over centuries, reflecting the unique intellectual traditions of Chinese textual scholarship. Building upon this conceptual foundation, the paper investigates the developmental pathways of Chinese bibliography in the context of the digital intelligence era. [Results/Conclusions] To develop a world-class Chinese bibliography that embodies distinctive Chinese characteristics and achieves international excellence, a multi-dimensional strategy is required. First, efforts must be firmly grounded in the preservation and promotion of excellent traditional Chinese culture, drawing on the rich heritage of classical bibliographic scholarship while reinterpreting it for contemporary needs. Second, the discipline must adhere to Marxist scientific principles to ensure that theoretical development remains critically informed, historically aware, and socially relevant. Third, the social service function of Chinese bibliography should be strengthened, particularly in guiding nationwide reading initiatives, fostering public cultural literacy, and supporting lifelong learning in an increasingly digital society. Fourth, deepening international cultural exchanges and open cooperation is essential for mutual learning between Chinese and foreign bibliographic traditions, enabling Chinese bibliography to contribute actively to global conversations while maintaining its distinctive identity. Fifth, greater emphasis should be placed on practical applications in guiding bibliographic work within specialized fields, such as rare book cataloging, digital archives, library technical services, and the organization of born-digital resources. Sixth, technological transformations centered on digital resources must be actively advanced. Digital bibliography should seek breakthroughs from four aspects: standard development, technology application, integration of theory and practice, and service expansion. Efforts should focus on promoting the standardization of digital resource description, utilizing cutting-edge technologies to achieve intelligent organization and processing of resources, strengthening the construction of cataloging databases, and extending services to meet cultural needs, thereby realizing a smart transformation. Taken together, these integrated measures constitute a coherent roadmap for achieving the creative transformation and innovative development of Chinese bibliography in the new era. Ultimately, this study advocates for constructing an autonomous knowledge system that balances disciplinary independence with openness to global scholarship, thereby ensuring that Chinese bibliography continues to thrive as both a heritage discipline and a forward-looking field of inquiry.
[Purpose/Significance] The construction of an autonomous knowledge system for computational humanities is both a historical inevitability as this emerging interdisciplinary field matures in the data-driven, computational era. It is also a substantive response to the national strategy of developing Chinese characteristics-based philosophy and social sciences. Unlike previous prior surveys which treat digital humanities as a relatively unified field, this paper emphasizes the epistemological shift from "digital" to "computational" and elucidates how Chinese scholarship has developed and refined the concept of computational humanities since it was first named in 2021. Its significance lies in providing a systematic, three-dimensional framework for assessing the field's institutional maturity and in demonstrating how computational humanities can serve as a vehicle for the communication of the Chinese story and the conveyance of Chinese wisdom within a global perspective. [Method/Process] Building on a conceptual clarification of the differences between "computation" and "humanities," "data" and "digital," and "digital humanities" and "computational humanities," the study examined the field along three interrelated dimensions: the academic system, the disciplinary system, and the discourse system. At the academic level, the analysis traced the consolidation of "human expression"–human cultural activity recorded through symbolic systems–as the research object. This three-tier inquiry comprises "what happened," "what was expressed," and "how to evaluate". A five-layer methodological hierarchy was cosntructed, spanning datafication, semantic annotation, text mining, spatiotemporal and network analysis, and large-language-model-driven intelligent computation. The research paradigm is five-stage and governed by the dual criteria of technical reproducibility and humanistic interpretive force. At the disciplinary level, the study surveyed sub-disciplines formed under the "computational X" logic, alongside talent cultivation, journals, conferences, laboratories, and academic public accounts. The argument is grounded in five empirical case studies: empty-space ratios in Chinese landscape painting (618-2011), the historical origins of the Tunpu dialect, sentencing leniency in misdemeanor cases, the construction of a historical-bibliography knowledge base, and topic-modeling comparisons of pre-Qin Confucian classics. [Results/Conclusions] The findings indicate that the field of computational humanities has reached a strategic juncture for transitioning from fragmented practice to systematic development. The academic system has formed an internally coherent conceptual framework; the disciplinary system has acquired a recognizable map of "Computational X" branches; and the discourse system has produced an expressive structure unifying vertical hierarchies with horizontal connections, propelled by the strategic momentum of "AI+," "Data Elements ×," and the New Liberal Arts initiative. The limitations of this study include the incomplete systematization of the original theory, unsettled institutional affiliations, and limited capacity for international discourse. Our future research endeavors will be anchored in local issues and firmly embedded within the Chinese cultural milieu. This approach is expected to propel the autonomous refinement of the autonomous knowledge system through continuous, iterative processes of conceptual distillation and theoretical innovation.
