Encryption and dynamic port technology make the traditional traffic classification technology fail to meet the performance requirements of online game identification. In this study, an end-to-end traffic classification model based on auto-encoder dimension reduction is proposed to accurately identify online game traffic. First, the original traffic is preprocessed into a one-dimensional session flow quantity of 784 B, and the encoder is used for unsupervised dimension reduction and removing invalid features. Then, the parallel algorithm of the convolutional neural network and LSTM network is explored and constructed to extract and fuse spatial and temporal features of samples after dimension reduction. Finally, the fusion features are used for classification. When tested on the self-built game traffic dataset and the open dataset, the proposed model achieves an accuracy rate of 97.68% in online game traffic identification. Compared with the traditional end-to-end network traffic classification model, the model designed in this study is more lightweight and practical and can be easily deployed on devices with limited resources.