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Research on GCN Model for Traditional Chinese Medicine Constitution Identification Based on Facial Feature Extraction
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Qi ZHANG1, Kongfa HU1, 2, 3, Tao YANG1, Ziyan WANG1, Xiao HAN1, Anying XU1, Aihua GUAN1
Chinese Archives of Traditional Chinese Medicine | 2025, 43(8) : 9 - 12
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Chinese Archives of Traditional Chinese Medicine | 2025, 43(8): 9-12
• Digital Traditional Chinese Medicine •
Research on GCN Model for Traditional Chinese Medicine Constitution Identification Based on Facial Feature Extraction
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Qi ZHANG1, Kongfa HU1, 2, 3, Tao YANG1, Ziyan WANG1, Xiao HAN1, Anying XU1, Aihua GUAN1
Affiliations
  • 1.College of Artificial Intelligence and Information Technology，Nanjing University of Chinese Medicine，Nanjing 210023，Jiangsu，China
  • 2.Jiangsu Intelligent Traditional Chinese Medicine Health Service Engineering Research Center，Nanjing 210023，Jiangsu，China
  • 3.Jiangsu Major Health Risk Management and Traditional Chinese Medicine Prevention and Control Policy Research Center，Nanjing University of Chinese Medicine，Nanjing 210023，Jiangsu，China
Published: 2025-08-10 doi: 10.13193/j.issn.1673-7717.2025.08.003
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According to the relevant theories of traditional Chinese medicine，there are differences in popularity，blood，body fluid and other aspects of different constitutions，which affect their health and disease manifestations.In recent years，although the research based on TCM constitution identification has made breakthrough progress，it still faces many challenges.Therefore，this paper proposed a graph convolutional network（GCN）model of TCM constitution identification based on facial feature extraction，which combined data augmentation and GCN model to solve the problems of insufficient dataset and poor recognition ability of machine learning models.Firstly，data augmentation techniques were employed to expand the acquired image dataset，thereby enhancing the generalization ability and stability of model training.Subsequently，these images were segmented to construct a graph structure suitable for the graph convolutional network model.Finally，the model was sent to a Softmax classifier for classification.Experimental results indicated that the proposed model achieved accuracy and recall，and F1 scores were 77.97%，78.98% and 77.87% respectively in constitution identification，outperforming classical machine learning models for constitution identification.

TCM constitution  /  deep learning  /  GCN  /  accuracy  /  machine learning
张琪, 胡孔法, 杨涛, 王子琰, 韩笑, 徐安迎, 管爱华. 基于面部特征的中医体质辨识GCN模型研究. 中华中医药学刊, 2025 , 43 (8) : 9 -12 . DOI: 10.13193/j.issn.1673-7717.2025.08.003
Qi ZHANG, Kongfa HU, Tao YANG, Ziyan WANG, Xiao HAN, Anying XU, Aihua GUAN. Research on GCN Model for Traditional Chinese Medicine Constitution Identification Based on Facial Feature Extraction[J]. Chinese Archives of Traditional Chinese Medicine, 2025 , 43 (8) : 9 -12 . DOI: 10.13193/j.issn.1673-7717.2025.08.003
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Year 2025 volume 43 Issue 8
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doi: 10.13193/j.issn.1673-7717.2025.08.003
  • Online Date：2026-10-30
  • Published：2025-08-10
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    1.College of Artificial Intelligence and Information Technology，Nanjing University of Chinese Medicine，Nanjing 210023，Jiangsu，China
    2.Jiangsu Intelligent Traditional Chinese Medicine Health Service Engineering Research Center，Nanjing 210023，Jiangsu，China
    3.Jiangsu Major Health Risk Management and Traditional Chinese Medicine Prevention and Control Policy Research Center，Nanjing University of Chinese Medicine，Nanjing 210023，Jiangsu，China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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