摘要The digitization of traditional Chinese medicine(TCM)has generated vast amounts of data.However,these data are characterized by significant heterogeneity and complex semantic structures,posing substantial challenges for systematic integration and intelligent analysis,and limiting its potential for modern clinical and computational research.To address the challenges posed by the high heterogeneity and complex structure in TCM data,we designed and developed the TCM Data Hub platform,which is powered by the YiYuan large language models(LLMs).This platform aims to enhance intelligent data processing capabilities and unlock the potential for clinical application of TCM data through systematic integration and efficient utilization,thereby bridging the gap between traditional knowledge and modern computational research.This study first analyzed the heterogeneity and complexity of TCM infor-mation with respect to data types,structures,and semantics.A standardized data framework was constructed to enhance data inte-gration and interoperability.Based on the TCM Intelligent Computing Platform of the China Academy of Chinese Medical Sciences,we trained the YiYuan LLMs to acquire domain-specific semantic understanding of TCM,thereby improving the platform's compre-hension of specialized terminology and knowledge systems.Leveraging the natural language processing capabilities of the LLMs,we developed a human-in-the-loop data processing system to enable efficient extraction,cleansing,and structured organization of TCM data.In addition,utilizing Vue and Java technologies,we developed multiple LLM-powered intelligent agents and systems,including a human-in-the-loop data processing system,as well as automated prescription mining and network pharmacology anal-ysis agents.Task-specific agents tailored to TCM data processing were developed to enhance the model's effectiveness in clinical knowledge discovery.System functionality and platform infrastructure were implemented using Java and Vue technologies.The TCM Data Hub platform has completed system construction and core functionality implementation.It supported integrated management and efficient access to 8 key types of TCM data:prescriptions,materia medica,ingredients,targets,diseases(Western medicine),diseases(TCM),syndromes,and therapeutic methods.The human-in-the-loop data processing system achieved an accuracy of 95.34％in structuring TCM data and supported annotation for data requiring manual labeling.The intelligent agent-driven big-data analytics module enabled 1-click,end-to-end workflows for TCM prescription mining,herb-syndrome association analysis,network pharmacology,and molecular biology research,completing a full data mining task in approximately 30 minutes.Users can interact with and manipulate data through a visual front-end interface.The system demonstrated stable performance,strong scalability,and a user-friendly experience.Empowered by the YiYuan LLMs,the TCM Data Hub platform significantly improves the accessibility,usability,and intelligence of TCM data.It effectively bridges traditional TCM knowledge with modern intelligent technologies,provid-ing robust data support and intelligent tools for TCM research and clinical applications.