History of Chinese medicine is a compulsory course in traditional Chinese medicine (TCM) universities and colleges. Its complex knowledge system poses certain challenges to teaching. Existing research on TCM knowledge graphs has not covered the systematic knowledge involved in this course. This study aims to leverage modern information technology to achieve knowledge structuring and visualization of the History of Chinese Medicine textbook, thereby supporting TCM education. By employing a large language model combined with manual verification, a bottom-up approach was adopted to construct the knowledge graph, which was then stored in the Neo4j graph database. A knowledge graph containing 2 879 entities and 2 487 semantic relationships was successfully built. The model achieved a triplet extraction precision of 65.42%, recall of 77.82%, and an F1 score of 71.09%, demonstrating high consistency between manual verification and model extraction results. This knowledge graph provides teaching visualization and associative retrieval support, possesses diverse educational potential, and offers a reference example and framework for intelligent innovation in medical humanistic education.
Liu Siyan
,
Zhou Yaqian
,
Chu Jiaqi
,
Jiang Yutong
,
Sun Lingzhi
. Construction of a knowledge graph for the textbook History of Chinese Medicine based on large language models and Neo4j[J]. Chinese Journal of Medical Education, 2026
, 46(7)
: 515
-520
.
DOI: 10.3760/cma.j.cn115259-20251102-01390
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