Abstract: Text sentiment classification is a hot topic in the field of natural language processing in recent years. It aims to analyze the subjective sentiment polarity of text. More and more attention has been paid to the problem of fine grained sentiment classification based on specific aspects. In traditional deep models, the attention mechanism can significantly improve the classification performance. Based on the characteristics of Chinese language, a deep model combining multi-hop attention mechanism and convolutional neural network (MHA-CNN) is proposed. The model makes use of the multidimensional combination features to remedy the deficiency of one dimensional feature attention mechanism, and can get deeper aspect sentiment feature information without any prior knowledge. Relative to the attention mechanism based long short-term memory (LSTM) network, the model has smaller time overhead and can retain word order information of the characteristic part. Finally, we conduct experiments on a network open Chinese data set (including 6 kinds of field data), and get better classification results than the ordinary deep network model, the attention-based LSTM model and the attention-based deep memory network model.