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<p>Traditional Chinese Medicine (TCM) is a vital component of the Chinese heritage, embodying a wealth of medical knowledge and distinctive therapeutic practices. A critical challenge in TCM modernization lies in extracting essential information from its complex and diverse knowledge system to develop knowledge-based services, which represents a cutting-edge research focus. This study proposes a Large Language Model (LLM)-driven approach for structuring TCM knowledge integrating historical TCM texts with open-source TCM datasets. A Fine-Tuning ChatGLM3-6B (FT-ChatGLM3) model was developed on the AliCloud DSW platform, optimized specifically for Chinese-language processing to enhance semantic understanding and knowledge extraction within TCM contexts. FT-ChatGLM3 powers an intelligent TCM Q&amp;A system, significantly improving the accuracy and efficiency of diagnosis and therapeutic recommendations. Furthermore, a BERT-based TCM Entity Recognition (TCMER) model was developed, and a knowledge graph was constructed using FT-ChatGLM3’s outputs. Experimental results demonstrate that FT-ChatGLM3 achieves strong performance in TCM applications, delivering precise diagnosis and treatment suggestions. The TCMER model also exhibits high efficacy, facilitating the systematization and structuring of TCM knowledge, while improving knowledge retrieval and consistency. The integration of FT-ChatGLM3 and TCMER not only accelerates the development of TCM knowledge graphs but also advances TCM modernization and its intelligent application in global healthcare.</p> Graphical Abstract <p></p>

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Construction and Application of Traditional Chinese Medicine Knowledge Graph Based on Large Language Model

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Bo Zhang,
  • Ruifang Li,
  • Kedong Yin,
  • Shuo Hua,
  • Shiyu Li,
  • Mengwan Jiang,
  • Haoping An,
  • Peng Li

摘要

Traditional Chinese Medicine (TCM) is a vital component of the Chinese heritage, embodying a wealth of medical knowledge and distinctive therapeutic practices. A critical challenge in TCM modernization lies in extracting essential information from its complex and diverse knowledge system to develop knowledge-based services, which represents a cutting-edge research focus. This study proposes a Large Language Model (LLM)-driven approach for structuring TCM knowledge integrating historical TCM texts with open-source TCM datasets. A Fine-Tuning ChatGLM3-6B (FT-ChatGLM3) model was developed on the AliCloud DSW platform, optimized specifically for Chinese-language processing to enhance semantic understanding and knowledge extraction within TCM contexts. FT-ChatGLM3 powers an intelligent TCM Q&A system, significantly improving the accuracy and efficiency of diagnosis and therapeutic recommendations. Furthermore, a BERT-based TCM Entity Recognition (TCMER) model was developed, and a knowledge graph was constructed using FT-ChatGLM3’s outputs. Experimental results demonstrate that FT-ChatGLM3 achieves strong performance in TCM applications, delivering precise diagnosis and treatment suggestions. The TCMER model also exhibits high efficacy, facilitating the systematization and structuring of TCM knowledge, while improving knowledge retrieval and consistency. The integration of FT-ChatGLM3 and TCMER not only accelerates the development of TCM knowledge graphs but also advances TCM modernization and its intelligent application in global healthcare.

Graphical Abstract