In this paper, we proposed an abnormal event recognition model based on surveillance video. The model can monitor the foreground target in video in real time and determine whether there is an abnormal event by analyzing the motion information of the target. The model utilizes the background model-based hybrid Gaussian algorithm to extract the foreground target. The L-K feature point tracking algorithm based on the gold tower iteration is subsequently adopted to obtain the foreground optical flow motion information. The abnormal event is judged by the analysis of the foreground area ratio, speed variance, and the overall entropy. Two kinds of abnormal events, such as explosions, short-time crowding and dispersion are chosen for simulation, the results show that the model can accurately extract the foreground target area and correctly determine the occurrence of abnormal events. Furthermore, the method can quickly and accurately identify abnormal events in the surveillance video, and can help the management department to find and control abnormal events in time.