Current traffic classification methods often suffer from poor classification effects on minority classes when facing class imbalanced flows. To solve this problem, an end-to-end classification model for class-unbalanced encrypted traffic is proposed. The proposed model adds an Inception module to the traditional CNN model for feature fusion, so that the model can extract richer features, which makes up for the lack of feature learning caused by the small number of samples in a minority of categories; at the same time introduces a The channel-spatial domain attention module assigns corresponding weights to the features fused by the Inception module according to their importance, so that the model pays more attention to more important features and enhances the characterization ability of traffic features. At the same time, in order to reduce network parameters, a combination of convolutional layer and global average pooling layer is used to replace the fully connected layer in the model. The experimental results show that compared with the current typical traffic classification model, the proposed model has better classification performance on the minority classes of the data set, and the accuracy rate, recall rate and F1-Score have been significantly improved. And the comprehensive performance index F1-Score is on some minority classes. The improvement reached 15%~18%.