Aiming at the problem that it is difficult to identify the encrypted traffic， a novel detection method based on the chain-building information is proposed， which utilize the a neural network to extract encrypted traffic characteristics from chain-building data. Firstly the interactive traffic between clients and servers is captured at the beginning of the encrypted connection establishment， then the fore 1 024 bytes of them is converted into grayscale. Finally the convolutional neural network model is constructed to learn these characteristics to extract the pattern of the encrypted traffic. Due to the category information can be extracted at the stage， so this method has the characteristic of early identification， which enables the identification and management of encrypted traffic to be organically combined. In addition， in view of the problems from infinite background traffic attribute set and incomplete training data， an approximate complete method is proposed which mixs random data to the background traffic for data enhancement. The test is carried out in a real environment， the results show that the accuracy of this method reaches 95.4%， and the recognition time is 0.1 ms， which is significantly better than comparison algorithms.