Aiming at the problem that a single neural network model has a simple structure and traditional attention models cannot effectively extract hierarchical text features

propose a text sentiment classification based on HAN and two-channel composite model.Firstly

a bidirectional gated recurrent unit neural network is used to extract serialized information on a channel

and a hierarchical attention network（HAN）is introduced to learn serial hierarchical text information.Secondly

in the other channel

use the convolutional neural network（CNN）to obtain local text features

and use HAN to learn deep-level feature information.Finally

the two channels are fused to enrich the feature vectors

optimize effect of text sentiment classification and improve the accuracy of the model.Comparative experiments on three Chinese data sets show that the accuracy of the models in this paper is 92.06 %

91.08 %

and 92.71 %

respectively.It proves that the proposed model perform better than single channel model

the effect of the HAN is better.