The invention discloses a method of application classification in Tor anonymous communication flow, which mainly solves the problem of acquisition of upper-layer application type information in the Tor anonymous communication flow and relates to the correlation technique, such as feature selection, sampling preprocessing and flow modeling. The method comprises the following steps of: firstly, defining a concept of a flow burst section by utilizing a data packet scheduling mechanism of Tor, and serving a volume value and a direction of the flow burst section as classification features; secondly, preprocessing a data sample based on a K-means clustering algorithm and a multiple sequence alignment algorithm, and solving the problems of over-fitting and inconsistent length of the data sample through the manners of value symbolization and gap insertion; and lastly, respectively modeling uplink Tor anonymous communication flow and downlink Tor anonymous communication flow of different applications by utilizing a Profile hidden Markov model, providing a heuristic algorithm to establish the Profile hidden Markov model quickly, during specific classification, substituting features of network flow to be classified into the Profile hidden Markov models of different applications, respectively figuring up probabilities corresponding to an uplink flow model and a downlink flow model, and deciding the upper-layer application type included by the Tor anonymous communication flow to be classified through a maximum joint probability value.