Abstract: Text classification which is often used in information retrieval,emotion analysis and other fields,is a very important content in the field of natural language processing and has become a research hotspot of many scholars.Traditional text classification model exists the problems of incomplete text feature extraction and weak semantic expression,thus,a text classification model based on improved TF-IDF algorithm and attention base on Bi-LSTM and CNN (ABLCNN) is proposed.Firstly,the TF-IDF algorithm is improved by using the distribution relationship of feature items within and between classes and location information to highlight the importance of feature items,the text is represented by word vector trained by word2vec tool and improved TF-IDF.Then,ABLCNN extracts the text features.ABLCNN combines the advantages of attention mechanism,long-term memory network and convolutional neural network.ABLCNN not only extracts major the context semantic features of the text,but also takes into account the local semantic features,At last,the feature vector is classified by softmax function.Chinese text classification model based on improved TF-IDF and ABLCNN is tested on THUCNews dataset and online_ shopping_ 10_cats dataset.The results of experimental show that the accuracy on the THUCNews dataset is 97.38% and the accuracy on the online_ shopping_ 10_cats dataset is 91.33%,the accuracy of experiment is higher than that of other text classification models.

Key words: Attention, Convolutional neural network, Long-term and short-term memory network, Term frequency-inverse document frequency, Text classification