The identification and classification of traffic is of great significance for maintaining network security, optimizing network management and providing reliable service quality. These functions not only help prevent malicious activities such as network attacks and illegal intrusions, but also effectively support the reasonable allocation of network resources and improve user experience. However, although the wide application of network traffic encryption technology enhances the security of data transmission, it also makes the content of traffic difficult to be directly analyzed, resulting in the existing identification technology is inefficient in the face of encrypted traffic and difficult to accurately classify. This not only affects the maintenance of network security, but also limits the further improvement of network service quality. Therefore, developing efficient and accurate encryption traffic identification methods has become an urgent problem to be solved. However, the existing work still has three main inherent limitations: (1) The potential relationship between the flow load feature and the sequence feature is ignored in the feature extraction process. (2) To adapt to the characteristics of different protocols to ensure the accuracy and robustness of encrypted traffic identification. (3) Training effective deep learning models requires large amounts of manually labeled data. This study aims to propose a method of encrypted traffic recognition based on CLSTM (a combination of 2-conv CNN and BiLSTM) and Mean Teacher collaborative learning. By detecting the fusion features of traffic load features and sequence features, the accuracy and robustness of encrypted traffic identification are improved, and the dependence of the model on labeled data is reduced. The experimental results show that the proposed CLSTM-MT collaborative learning method not only outperforms the traditional methods in the task of encrypted traffic identification and classification, but also improves the performance of the model by using only a small amount of labeled data when the cost of data labeling is high.