Abstract:

[Purpose/Significance] Against the backdrop of rapid technological change and industrial transformation, China's labor market is undergoing profound restructuring, giving rise to multi-dimensional mismatches in the scale, structure, skills, and spatial distribution of talent supply and demand. Traditional governance models, which are largely dependent on statistical reporting and periodic planning, are increasingly inadequate in terms of timeliness, granularity, and evidence support. In response, this study focuses on matching talent supply and demand and integrates situation awareness theory with the intelligence-processing paradigm of "from information to decision." We propose a five-layer dynamic perception framework - "Data-Perception-Comprehension-Projection-Decision" - tailored to talent governance. The framework addresses the limitations of existing research, which often focuses on static measurements and neglects external events and policy shocks. It establishes a governance-oriented theoretical tool that meets major national strategic needs. By incorporating an event-driven dynamic sensing mechanism, the framework enables real-time identification and forward-looking regulation of talent mismatches in complex and evolving environments. [Method/Process] Theoretically, the study builds on Endsley's situation awareness model and further incorporates intelligence processes, causal inference, and complex systems theory, expanding the traditional "perception-comprehension-projection" cognitive sequence into a social closed loop governance system anchored by a robust data foundation and decision-feedback mechanism. Technically, we construct an event ontology and slot schema specific to talent supply-demand scenarios. Through event extraction and large language model-based semantic reasoning, the system generates structured "event-entity-relation" knowledge units from heterogeneous sources such as policy documents, industry reports, and job postings. Knowledge is then aligned and integrated using retrieval-augmented generation, domain knowledge graphs, and authoritative classification standards. At the modeling level, the framework integrates efficiency assessment, causal inference, and risk analysis into a unified "situation comprehension-situation projection" analytical architecture. Methods such as stochastic frontier analysis, difference-in-differences, and multi-agent simulation are employed to examine talent allocation efficiency, policy impacts, and supply-demand evolution. In application, a prototype system - combining data governance, event sensing, early warning, and decision support - is developed and tested using higher education major-industry matching as an empirical case. [Results/Conclusions] This research has developed an operational and verifiable theoretical and technical framework for the dynamic sensing of talent supply-demand alignment. It establishes a coherent chain that spans from "multi-source data integration" to "event-driven sensing," "causal mechanism analysis," "medium- and long-term trend projection," and "policy scenario evaluation," thereby facilitating a transformation in talent governance from static planning to dynamic monitoring, and from experience-based judgment to evidence-driven decision-making. The proposed system not only converts heterogeneous multi-source data into computable event knowledge, but also identifies the root causes of mismatches through causal inference and efficiency assessment. Moreover, it delineates potential future trajectories via trend forecasting and risk alerts. Building upon this foundation, decision-makers can leverage scenario simulation and strategy recommendations to obtain quantitative evidence and actionable plans for program restructuring, enrollment planning optimization, and curriculum reform. Nevertheless, the study has several limitations, including uneven data quality across sources, model dependence on historical samples, and the need for more fine-grained representation of complex behavioral mechanisms. Future research could build on the current work in several directions. For instance, it could incorporate richer data on individual behaviors and organizational strategies to enhance the model's ability to depict micro-level decision-making processes. It could also deepen the integration of causal inference with knowledge-graph techniques to improve the accuracy and clarity of identifying the effects of complex policy portfolios.

Key words: talent supply-demand matching, dynamic perception, data governance, event extraction, knowledge graph