[1]董云春,晏峻峰*.融合知识图谱与图卷积神经网络中医证型分类模型[J].计算机技术与发展,2025,(03):140-147.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0341]
　DONG Yun-chun,YAN Jun-feng*.A Classification Model of TCM Syndrome Based on Integration of Knowledge Graph and Graph Convolutional Network[J].,2025,(03):140-147.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0341]
点击复制

融合知识图谱与图卷积神经网络中医证型分类模型()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年03期
页码:
140-147
栏目:
人工智能
出版日期:
2025-03-10

文章信息/Info

Title:
A Classification Model of TCM Syndrome Based on Integration of Knowledge Graph and Graph Convolutional Network
文章编号:
1673-629X(2025)03-0140-08
作者:
董云春; 晏峻峰*
湖南中医药大学 信息科学与工程学院,湖南 长沙 410208
Author(s):
DONG Yun-chun; YAN Jun-feng*
School of Informatics,Hunan University of Chinese Medicine,Changsha 410208,China
关键词:
图卷积神经网络; 证型分类; 知识图谱; 证素; 残差结构
Keywords:
graph convolutional network; syndrome classification; knowledge graph; status element; residual structure
分类号:
TP301
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0341
摘要:
中医证型分类是中医辨证体系中极为重要的一部分,证型分类的准确性会影响到中医诊疗的效果,因此如何提高证型分类的准确率一直是中医学研究的一个重要课题。 利用人工智能技术探究中医证型分类模型,提高证型分类性能,从而为中医学的其他相关应用奠定基础。 该文采用了一种融合模型———KGRGCN。 该模型能够高效地获取数据节点之间的深层特征。 图卷积神经网络作为一种处理图结构数据的有效方法,通过残差结构的引入,可以提高模型的表达能力和训练稳定性;同时融合证型相关知识图谱,辅助模型整合证型嵌入表示,增强症状与证型间的关联关系;为了进一步增强模型性能,提出增加证素权重作为其桥梁的策略,融合多层信息表征,并采用多层感知机(MLP)来进行证型分类。 实验结果表明,KGRGCN 模型在证型分类任务中表现优异。 具体而言,该模型的准确率为 75. 43% ,精确率为 74. 93% ,召回率为 76. 91% ,F1-score 为 75. 91% 。 这些结果表明,模型在分类性能上优于几种流行的分类方法,包括支持向量机、TextCNN 和随机森林等。
Abstract:
The classification of traditional Chinese medicine (TCM) syndrome is an integral component of the TCM diagnostic system.The accuracy of syndrome classification affects the effectiveness of TCM diagnosis and treatment,so how to improve the accuracy of syndrome classification has always been an important topic in TCM research. Leveraging artificial intelligence technologies to explore TCM syndrome classification models aims to enhance the performance of syndrome classification,thereby laying the groundwork for other related applications in TCM. We employ a hybrid model—KGRGCN,which is capable of efficiently capturing the deep features between data nodes. Graph Convolutional Networks ( GCN) are effective for handling graph - structured data, and by incorporating residual structure,the model’s expressive power and training stability are significantly enhanced. Integrating syndrome-related knowledge graphs helps the model merge syndrome embeddings,thereby enhancing the relationships between symptoms and syndromes. To further enhance model performance,we propose a strategy that incorporates status element weights as a bridge,integrating multi-layer information repre-sentations. Additionally,a Multi-Layer Perceptron (MLP) is employed for syndrome classification. This approach aims to leverage the combined strength of diverse information layers and the classification power of MLP to achieve more accurate and robust results in syndrome analysis. The experimental results indicate that the KGRGCN model proposed performs excellently in syndrome classification tasks. Specifically,the model achieved an accuracy of 75. 43% , a precision of 74. 93% , a recall of 76. 91% , and an F1 - score of 75.91% . These results demonstrate that the model outperforms several popular classification methods,including SVM,TextCNN,and Random Forests,in terms of classification performance.

相似文献/References:

[1]刘 臣,李 秋,郝宇辰.基于图卷积神经网络的在线社区行为预测[J].计算机技术与发展,2021,31(04):28.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 005]
　LIU Chen,LI Qiu,HAO Yu-chen.Prediction of Collaborative Behavior of Users in Online KnowledgeCommunities Based on Graph Convolutional Neural Network[J].,2021,31(03):28.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 005]
[2]李社蕾,杨博雄,陆娇娇.谱图傅里叶变换与谱图小波变换基分析研究[J].计算机技术与发展,2021,31(05):85.[doi:10. 3969 / j. issn. 1673-629X. 2021. 05. 015]
　,,et al.ＡｎａｌｙｓｉｓａｎｄＳｔｕｄｙｏｆＳｐｅｃｔｒａｌＦｏｕｒｉｅｒＴｒａｎｓｆｏｒｍ ａｎｄＳｐｅｃｔｒａｌＷａｖｅｌｅｔＴｒａｎｓｆｏｒｍ Ｂａｓｉｓ[J].,2021,31(03):85.[doi:10. 3969 / j. issn. 1673-629X. 2021. 05. 015]
[3]建兵,杨 超*,刘方方,等.基于图卷积神经网络和 RoBERTa 的物流订单分类[J].计算机技术与发展,2023,33(10):195.[doi:10. 3969 / j. issn. 1673-629X. 2023. 10. 030]
　WANG Jian-bing,YANG Chao*,LIU Fang-fang,et al.A Logistics Order Classification Method Based on Graph Convolution Network and RoBERTa[J].,2023,33(03):195.[doi:10. 3969 / j. issn. 1673-629X. 2023. 10. 030]
[4]张 艳,肖文琛,张 博.基于双流骨架信息的人体动作识别方法[J].计算机技术与发展,2024,34(01):158.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 023]
　ZHANG Yan,XIAO Wen-chen,ZHANG Bo.Human Action Recognition Method Based on Two-flow Skeleton Information[J].,2024,34(03):158.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 023]
[5]张茜茜,陈 平.基于图卷积的离子液体 CO2 溶解度可解释性预测[J].计算机技术与发展,2024,34(02):134.[doi:10. 3969 / j. issn. 1673-629X. 2024. 02. 020]
　ZHANG Qian-qian,CHEN Ping.Interpretable Prediction of CO2 Solubility of Ionic Liquids Based on Graph Convolution Neural Network[J].,2024,34(03):134.[doi:10. 3969 / j. issn. 1673-629X. 2024. 02. 020]
[6]朱 越,黄海于,罗学义.基于实时视频流的 3D 人体姿势和形状估计[J].计算机技术与发展,2024,34(04):42.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 007]
　ZHU Yue,HUANG Hai-yu,LUO Xue-yi.3D Human Pose and Shape Estimation Based on Live Video Streams[J].,2024,34(03):42.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 007]
[7]董文永,梁智学,周孟强,等.融合时间编码和时间分布注意力的推理模型[J].计算机技术与发展,2025,(06):182.[doi:10.20165/j.cnki.ISSN1673-629X.2025.0014]
　DONG Wen-yong,LIANG Zhi-xue,ZHOU Meng-qiang,et al.A Reasoning Model Integrating Time Encoding and Time Distribution Attention[J].,2025,(03):182.[doi:10.20165/j.cnki.ISSN1673-629X.2025.0014]
[8]胡翼,于海,郭鑫,等.融合句法信息的实体关系联合抽取[J].计算机技术与发展,2024,34(08):93.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0149]
　HU Yi,YU Hai,GUO Xin,et al.Syntactic Information Fused Joint Entity Relation Extraction[J].,2024,34(03):93.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0149]
[9]曹茂俊,王瑞芳,张国良.基于图神经网络的测井领域知识图谱实体对齐方法[J].计算机技术与发展,2025,(04):121.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0348]
　CAO Mao-jun,WANG Rui-fang,ZHANG Guo-liang.Entity Alignment Method for Logging Domain Data Asset Knowledge Graph Based on Graph Neural Networks[J].,2025,(03):121.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0348]

更新日期/Last Update: 2025-03-10