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Academic Journal of Computing & Information Science, 2025, 8(1); doi: 10.25236/AJCIS.2025.080105.

Research on Traditional Chinese Medicine Prescription Recommendation Method Based on Multi-Graph Convolutional Neural Networks

Author(s)

Jun Tan, Ling Liu, Peng Dong, Chunyu Xing

Corresponding Author:
​Jun Tan
Affiliation(s)

Bozhou Vocational and Technical College, Bozhou, China

Abstract

To improve the recommendation performance of Traditional Chinese Medicine (TCM) prescriptions, a recommendation method based on multi-graph convolutional neural networks is proposed. First, the relationships between herbs and symptoms are represented by constructing homogeneous and heterogeneous graphs. Then, information from high-order neighboring nodes is recursively propagated to update the representation of each node. Finally, the syndrome representation of the patient is obtained through feature fusion and induction, which is used to recommend appropriate TCM prescriptions. Experimental results show that the recommendation method based on multi-graph convolutional neural networks significantly outperforms topic recommendation models and the general neural network models in terms of precision and recall.

Keywords

Graph Convolutional Neural Network; TCM prescriptions; Recommendation model; Heterogeneous graph

Cite This Paper

Jun Tan, Ling Liu, Peng Dong, Chunyu Xing. Research on Traditional Chinese Medicine Prescription Recommendation Method Based on Multi-Graph Convolutional Neural Networks. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 1: 32-37. https://doi.org/10.25236/AJCIS.2025.080105.

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