现代信息科技2025，Vol.9Issue(6)：39-45,7.DOI:10.19850/j.cnki.2096-4706.2025.06.008
基于卷积神经网络的中医医案诊断分类方法
Classification Method of TCM Medical Records Diagnosis Based on Convolutional Neural Network
摘要
Abstract
Aiming at the problems of insufficient context semantic capture,difficulty in effectively capturing long-distance dependence information and low classification accuracy in the study of TCM medical records diagnosis and classification,a hybrid model combining Text Convolutional Neural Network(TextCNN)and Gated Recurrent Unit(GRU)is proposed.Firstly,the Word2Vec model is used to train the word vector and construct the local word vector library.Secondly,the TextCNN is used to extract the text features of TCM medical records to capture local important information.Finally,the GRU is used to model the context information of the extracted features,thereby significantly enhancing the model's ability to process long dependencies.The experimental results show that the model performs well in the text classification task of TCM medicals records diagnosis,the prediction accuracy reaches 85.01%,and the F1 value is 81.86%.关键词
中医医案/TextCNN/GRU/Word2Vec模型/文本分类Key words
TCM medical record/TextCNN/GRU/Word2Vec model/text classification分类
信息技术与安全科学引用本文复制引用
邱雪峰,查青林,苗震,刘明,李欣依..基于卷积神经网络的中医医案诊断分类方法[J].现代信息科技,2025,9(6):39-45,7.基金项目
江西省科技厅重点研发计划项目(20171ACG70011) （20171ACG70011）
江西省科技厅重点研发计划项目(20203BBG72W008) （20203BBG72W008）
