计算机与现代化Issue(3)：1-5,5.DOI:10.3969/j.issn.1006-2475.2025.03.001
基于多特征融合的抑郁症识别模型构建
Construction of Depression Recognition Model Based on Multi-Feature Fusion
摘要
Abstract
In recent years,depression has become the primary problem of global mental health burden.In order to identify it,this paper proposes a depression recognition model combining BERT,BiLSTM and ConvNeXt.Firstly,the BERT model is used to generate feature vectors with rich semantics.Secondly,the BiLSTM,and ConvNeXt model is used to obtain the context infor-mation and the local features of the text,respectively.Thirdly,to alleviate the loss of semantic information in the feature extrac-tion process,the context and local learned by BiLSTM and ConvNeXt models are fused through residual connections.Finally,de-pression is recognized according to the fused feature information.The experimental results show that the proposed model improves the accuracy,recall and F1 value compared with other deep learning models,which can effectively extract the depression fea-tures of the text and improve the accuracy of depression recognition.关键词
抑郁症/BERT/BiLSTM/ConvNeXt/识别Key words
depression/BERT/BiLSTM/ConvNeXt/recognition分类
计算机与自动化引用本文复制引用
侯梦晗,韦昌法..基于多特征融合的抑郁症识别模型构建[J].计算机与现代化,2025,(3):1-5,5.基金项目
湖南省学位与研究生教学改革研究项目(2022JGYB142) （2022JGYB142）
湖南省教育厅科学研究项目(23A0312) （23A0312）
湖南中医药大学研究生创新课题项目(2024CX084) （2024CX084）
