关键词: 生成式AI, 历史文献, 数字阅读推广, 风险治理, 文化遗产

Abstract:

[Purpose/Significance] With the growing demand for intelligent cultural services, libraries are seeking innovative approaches to enhance access to and engagement with historical literature. Generative AI presents promising opportunities for transforming digital reading services, particularly in processing, interpreting, and promoting complex historical documents. This study investigates the integration of generative AI into library-based historical literature promotion, aiming to address persistent access limitations, foster more interactive user experiences, and optimize the depth and breadth of reading engagement. [Method/Process] This research adopts a multi-method approach combining literature review, comparative platform observation, and empirical implementation practice. The study focuses primarily on Shanghai Library's historical digital collections and AI-enabled services. It develops a structured three-layered implementation framework encompassing the data layer, application layer, and service layer-each mapped to corresponding technical and operational phases of digital reading promotion. Within this architecture, a six-step service pathway is articulated: demand analysis, activity planning, content mining, multimodal interaction, content review, and intelligent recommendation. Extensive practical experimentation is conducted across these stages. Key innovations include the application of Retrieval-Augmented Generation (RAG) to support complex historical document Q&A; the use of multimodal creative tools (e.g., Midjourney) to generate engaging visual materials; implementation of voice-based AI interactions to improve accessibility for diverse user groups; and the deployment of dynamic content management modules for librarians to curate and monitor AI-generated materials. Additionally, backend tools such as user profiling dashboards, personalized push notification systems, and topic-based knowledge repositories are developed and tested to enhance librarians' ability to deliver targeted and data-driven reading promotions. [Results/Conclusions] The findings demonstrate that generative AI significantly enhances the efficiency, precision, and user engagement levels of historical literature services. AI-driven methods substantially improve OCR accuracy, streamline metadata generation, facilitate both visual and semantic content creation, and enable real-time interactive services via natural language interfaces. These advancements contribute to a more immersive and responsive digital reading experience. However, several challenges persist, including limited availability of domain-specific training data, the ongoing risk of AI-generated content inaccuracies (hallucinations), and unresolved intellectual property considerations. The study emphasizes the importance of developing domain-specific large language models, establishing expert-assisted validation mechanisms, and formulating clear legal and ethical guidelines for AI-generated content in the library context. While the prototype platform developed in this research exhibits notable gains in user engagement and librarian workflow support, its long-term sustainability hinges on fostering cross-institutional resource collaboration, advancing supportive policy frameworks, and embedding robust ethical safeguards. Future research directions include the exploration of adaptive AI training systems incorporating user feedback loops, integration of cross-library data resources, and the enhancement of multilingual AI capabilities to better serve diverse and global user communities.

Key words: Generative AI, historical documents, digital reading promotion, risk governance, cultural heritage