The invention discloses a text classification method based on a bidirectional cyclic attention neural network, and belongs to the technical field of learning and
natural language processing. The method comprises the following steps: step 1, preprocessing data; Step 2, according to the preprocessed data, generating and training a word vector of each word through a Word2vec method; Step 3, performing text
semantic feature extraction on the word vector according to the word vector, fusing an attention mechanism and a bidirectional
recurrent neural network, calculating the overall weight of each word, and converting the weight into an output value Y (4) of a model; And step 4, taking the
feature vector Y (4) as the input of a softmax classifier according to the
feature vector Y (4), and carrying out classification identification. According to the method, the attention mechanism is fused in the text
feature learning model, the effect of keywords can be effectively highlighted, the performance of the model is greatly improved, and the text classification accuracy is further improved.