Research On Chinese Text Classification Method Based On Attention Mechanism And Multi-feature Fusion

As the key technology of search engine,Chinese text categorization plays a very important role and value in mining high value Chinese text information efficiently and fully from the huge amount of Chinese text information on the Internet,which will meet the requirements of social development.Text features extraction affects the performance of Chinese text classification system directly,and is one of the core technical bases in text classification.It is of great value and significance to design and implement a new deep learning based text feature extraction algorithm model to identify Chinese text features better and improve the recognition ability of Chinese text features.Based on deep learning Chinese text categorization,this paper makes deep research on Chinese text feature extraction algorithm based on convolutional neural network,long short term memory network,attention mechanism and multi-feature fusion model.In view of the different contribution of different text features to text category recognition in Chinese text categorization task,an attention algorithm model based on semantic understanding is proposed.The attention weight is generated by the fusion and learning characteristics of the feature learning results of the text data from two adjacent time steps.In order to solve the problem that different text elements play different roles in Chinese text classification recognition,a feature difference enhancement attention algorithm model is proposed.By generating the attention weight,the important text elements play a more prominent role in the text recognition.In order to solve the problem of scattered and sparse position distribution of important text features in Chinese text,a multi-feature fusion Chinese text classification model is proposed.By combining the semantic attention algorithm,the long short term memory network(LSTM)and convolutional neural network(CNN),we extract the text features more comprehensively and carefully.In order to solve the problem of uneven distribution of the key features of Chinese in texts,a feature enhanced fusion Chinese text classification model is proposed.The feature extraction of attention mechanism is enhanced by two-layer LSTM and CNN to obtain more abundant and more comprehensive text features containing feature information.The validity of the four proposed algorithm is verified by the Chinese text classification experiments.