Abstract: This paper provides a systematic overview of the research advancements and future perspectives in encrypted traffic classification techniques utilizing deep learning. With the increasing encryption of network traffic, traditional content-based traffic classification methods have gradually become ineffective. Consequently, researchers have shifted their focus to classification approaches based on statistical features, behavioral patterns, and deep learning. The paper starts with a review of traditional methods, machine learning-based techniques, and deep learning-based approaches for encrypted traffic classification, followed by highlighting the application and effectiveness of deep learning models—such as Convolutional Neural Networks（CNN）, Generative Adversarial Networks（GAN）, and Graph Neural Networks（GNN）—in classifying encrypted traffic. Subsequently, the paper analyzes the key challenges in encrypted traffic classification, including data distribution shifts, overfitting, vulnerability to adversarial sample attacks, and the presence of excessive invalid nodes. Finally, the paper outlines several promising future research directions, including the development of real-time adaptive learning models, adversarial training techniques, federated learning frameworks, and enhancements to cross-domain big data models, all aimed at improving the robustness and generalization performance of encrypted traffic classification systems.