The invention relates to a text classification method based on phrase vectors, and belongs to the technical field of natural language processing. The method comprises the steps of obtaining a lexicalitem set by means of text word segmentation and part-of-speech tagging of words; constructing corresponding phrase vector representation for all lexical items through an auto-encoder based on a convolutional neural network; improving a hierarchical attention model; during sentence coding, proposing that a specific target vector is set for each sentence in the text; in combination with a global target vector, synthesizing all lexical items and attention scores of all lexical items according to a certain weight to obtain sentence codes; and finally, through a document coding layer, a document attention layer and a document classification layer in the hierarchical attention model, obtaining the probability that the document corresponds to each category, namely text classification. Compared with the prior art, the method has the advantages that the problem of insufficient lexical item semantic information can be solved, semantic information which is more complete and accurate than independent word vectors is provided, the most distinctive semantic feature of each text can be concerned, and the text classification accuracy is further improved.
Text classification method based on phrase vectors