In recent years, the convolutional neural network model is often used in the research of text emotion classification. However, most of researches ignore the emotional information carried by the text feature words themselves and the wrong segmentation of Chinese text. Aiming at this problem, a Dual-channel Convolutional Neural Network sentiment classification model fused with Sentiment Feature (SFD-CNN) is proposed. In the model, one channel is used to construct the semantic vector matrix of emotional features to get more emotional type information, and another channel is used to construct the text word vector matrix to reduce the impact of segmentation errors. The experimental results show that the accuracy of SFD-CNN model is as high as 92.94%, which is better than that of the unmodified model.