Abstract: Text representation and text feature extraction are essential procedures in natural language processing and directly affect text classification performance.The major output of the present work is the establishment of the BLSTM_MLPCNN neural network model whose inputs are character-level vector integrated with word vector.In this model,firstly the character-level vector is obtained from character via convolutional neural network (CNN),and is integrated with the word vector to compose the pre-training words embedded vectors (also an input to BLSTM model).Then the combination of the BLSTM model’s forward output,word embedded vector and backward output forms the document feature map,and finally the MLPCNN model is used to extract feature.The experiments on the pertinent datasets prove the classification performance of BLSTM_MLPCNN model is superior to CNN model,RNN model and CNN/RNN combinatorial model.

Key words: Bidirectional long short-term memory network(BLSTM), Character-level vector, Convolutional neural network(CNN), Multi-layer perceptron convolutional neural network(MLPCNN), Multi-layer perceptron(MLP), Word vector