The static gesture recognition speed is higher than that of dynamic gesture recognition for RGB-D (RGB-Depth) data, but redundancy gestures and repeated gestures lead to low recognition accuracy. In order to solve the problem, a static gesture recognition method based on locking mechanism was proposed. First, RGB data flow and the Depth data stream were obtained through Kinect equipment, then two kinds of data flow were integrated into human body skeleton data flow. Second, the locking mechanism was used to identify static gestures, and comparison and calculation were done with the established bone point feature model gesture library before. Finally, an "advanced programmers road" brain-training Web game was designed for application and experiment. In the experiments of six different movement gestures, compared with the static gesture recognition method, the average recognition accuracy of the proposed method was increased by 14.4%; compared with the dynamic gesture recognition method, the gesture recognition speed of the proposed method was improved by 14%. The experimental results show that the proposed method keeps the high speed of static recognition method, realizes the real-time recognition; and also improves the identification accuracy through eliminating redundant repeated gestures.