Event camera has attracted the attention of the majority of researchers due to the inspiration of biological vision, breaks the way of regular data acquisition in the field of computer vision, directly hits the pain point of RGB images, and brings the advantages that 2D image sensors cannot match. Event Camera brings the advantages of removing redundant information, fast sensing capability, high dynamic range sensitivity and low power consumption, while its asynchronous event data cannot be directly applied to existing computer vision processing modes. Therefore, this paper classify the data stream using the key event based classification method. This method detects corner events with important information and only extracts features of corner events. While retaining the important features of event and condensing the extraction of event stream features, the amount of computation for other events is effectively reduced. The preset gesture is recognized to verify the validity of this method, achieving an accuracy of 97.86%.