Aiming at the problems of fast classification and accurate identification of encrypted traffic in current network, a new feature extraction method for data stream was proposed. Based on the characteristics of sequential data and the law of the SSL (Secure Sockets Layer) handshake protocol, an end-to-end one-dimensional convolutional neural network model was adopted, and five-tuples were used to label the data stream. By selecting the data stream representation manner, the number of data packets, and the length of feature bytes, the key field positions of sample classification were located more accurately, and the features with little impact on sample classification were removed, so that the 784 bytes used by a single data stream during the original input were reduced to 529 bytes, which reduced 32% of the original length, and the classification of 12 encrypted traffic service types was implemented with the accuracy of 95.5%. These results show that the proposed method can reduce the original input feature dimension and improve the efficiency of data processing on the basis of ensuring the accuracy of the current research.