Text classification method based on cyclic convolution network

The invention discloses a text classification method based on a cyclic convolution network. The text classification method comprises the following steps: step 1, representing context vectors of all words by using a bidirectional cyclic network; step 2, combining the context vectors and a word vector of a current word into the representation of the current word; step 3, extracting the most important context information by using a maximum pool technology to obtain text representation; step 4, carrying out text classification by using the text representation. According to the method disclosed by the invention, more word order information in a text can be kept and a long-distance text dependence relation is captured; semantics of the words can be accurately described and the words and phrases, which have the greatest influences on the text classification, are found by the maximum pool technology, so that the text classification accuracy rate is effectively improved. A test shows that the efficiency of the method is averagely improved by 1% on the aspect of a plurality of text classification data sets.