Tor is the most widely used anonymous communication network. Tor has developed a series of pluggable transports (PTs) to obfuscate traffic and avoid censorship. These PTs use different traffic obfuscation techniques, and many of them have been maintained and updated. In order to achieve continual learning against PTs and their updates, this paper proposes an incremental learning model for Tor traffic detection. First, we analyzed several common traffic obfuscation techniques, including randomization, mimicry, and tunneling. A feature set was designed for Tor obfuscation traffic detection. Second, this paper constructs the Tor incremental learning framework and proposes edge exemplar enhancement to enhance the memory of trained models for previous classes. It can enhance the previous class memory of the model through edge feature enhancement and selective replay to alleviate the catastrophic forgetting problem of incremental learning. Finally, we combined public and self-collected datasets to simulate the development of Tor PTs and verify the effectiveness of our model. The experimental results show that the improved model in this paper has the highest accuracy rate of 87.6% in the simulated environment. This means that the incremental learning model can effectively cope with the updating of PTs.