Abstract: The emergence of traffic encryption technology brought new challenges to traffic classification. In order to classify the encrypted traffic quickly and accurately, an encrypted traffic classification method based on convolutional attention gated recurrent network was proposed. This model combines a convolutional neural network and a gated recurrent unit. According to the characteristics of the traffic data, the pooling layer of convolutional neural network was modified to extract the characteristics of single data packet.The key features of a single packet were found by attention mechanism and given high weight. Then, the time series characteristics between packets at the flow level were extracted by gated recurrent unit, which reflected the overall and local features of traffic from packet level and flow level. Experimental results show that this method improves the classification accuracy, real-time performance and training efficiency compared with the traditional methods.