Abstract: 【Purpose/significance】With the development of social media,the number of various kinds of news has surged,and the moni⁃toring and processing of public opinion has become more and more important.The efficient and accurate identification of public opin⁃ion news can help relevant departments timely collect and track the information of emergencies and deal with it,so as to reduce the im⁃pact of public opinion on the society.In this paper,a news title text classification model combining BERT,TEXTCNN and BILSTM is proposed, which takes full account of word embedding information, text features and context information to improve the accuracy of news title category recognition.【Method/process】The news title text vector generated by BERT is input to TEXTCNN to extract fea⁃tures,and the results of TEXTCNN are input to BILSTM to capture the news title context information,and Softmax is used to judge the
classification results.【Result/conclusion】The research shows that the proposed classification model,which combines the three algo⁃rithms of language model based BERT,word vector based TEXTCNN and context mechanism based BILSTM,achieves more than 0.92 in accuracy,precision,recall rate and F1 value,and has good generalization ability,which is superior to the traditional text classification model.【Innovation/limitation】This article uses the BERT word embedding,simultaneously feature extraction and capture context se⁃mantics,model recognition news category performance is good,but more model parameter vector dimension is larger for training equip⁃ment demand is higher,at the same time,the data category only 10 class,not more detailed data for more category or categories for ex⁃periments.