The anonymous system Tor uses an asymmetric algorithm to protect the content of communications, allowing criminals to conceal their identities and hide their tracks. This malicious usage brings serious security threats to public security and social stability. Statistical analysis of traffic flows can effectively identify and classify Tor flow. However, few features can be extracted from Tor traffic, which have a weak representational ability, making it challenging to combat cybercrime in real-time effectively. Extracting and utilizing more accurate features is the key point to improving the real-time detection performance of Tor traffic. In this paper, we design an efficient and real-time identification scheme for Tor traffic based on the time window method and bidirectional statistical characteristics. In this paper, we divide the network traffic by sliding the time window and then calculate the relative entropy of the flows in the time window to identify Tor traffic. We adopt a sequential pattern mining method to extract bidirectional statistical features and classify the application types in the Tor traffic. Finally, extensive experiments are carried out on the UNB public dataset (ISCXTor2016) to validate our proposal’s effectiveness and real-time property. The experiment results show that the proposed method can detect Tor flow and classify Tor flow types with an accuracy of 93.5% and 91%, respectively, and the speed of processing and classifying a single flow is 0.05 s, which is superior to the state-of-the-art methods.