A Knowledge Management Framework for Computational Analysis of Chinese Classical Literature

This study develops a multi-loop knowledge management framework for capturing and refining knowledge from unstructured text. A system-oriented design is applied to 80 classical Chinese texts and interaction data from 200 users. The framework integrates automated extraction, semantic representation, and human-in-the-loop feedback, evaluated via baseline comparison, performance metrics, and robustness tests. Multi-source feedback improves topic coherence, interpretability, reduces latency, and stabilizes knowledge base construction. The study extends knowledge management constructs, including absorptive capacity, dynamic capabilities, and adaptive knowledge governance, through a multi-loop feedback mechanism. The framework enhances knowledge traceability and supports decision-making in knowledge-intensive contexts. This study proposes a configurable knowledge management system architecture integrating iterative feedback loops for managing complex knowledge assets.