Research on the Construction Method of Famous TCM Physicians’ Academic Knowledge Graph Based on Multimodal Deep Learning — Application of the AGBAN Model

Huisi Hong, Yunchou Li, Yiming Yuan, Weihan Shen, Shiqi Luo, Jianlong Huang

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¹ The Tenth Clinical Medical College of Guangzhou University of Chinese Medicine; Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan 528401, Guangdong, China,

² Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China,

JCNR 2026 , 10(4), 416–424; https://doi.org/10.26689/jcnr.v10i4.14866

© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

Abstract

Aiming at the problems of knowledge fragmentation and opaque reasoning in the digital inheritance of famous TCM physicians’ academic thoughts and diagnosis-treatment experience, a multimodal knowledge graph construction method based on the AGBAN model is proposed. Using more than 3,000 outpatient medical records of famous TCM physicians as the data source, multimodal information is integrated to construct a clinical knowledge graph through ontology design, entity-relationship extraction, and knowledge storage. The graph attention network and reinforcement learning mechanism of the AGBAN model are introduced to optimize the diagnosis-treatment path. The results show that the knowledge graph contains 3,089 entities and 1,461 relationships, with an average degree of 2.49; the average reciprocal rank of link prediction of the AGBAN model is 0.973, which is 165.4% higher than that of the TransE model, the diagnosis success rate is 59.19%, and the average reasoning path is 5 steps; cluster analysis verifies the core TCM principles such as “drug-syndrome correspondence”. The conclusion indicates that this method realizes the structured representation and intelligent reasoning of famous TCM physicians’ clinical experience, providing a feasible path for TCM academic inheritance and clinical decision support.

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