As a core component of urban transportation, the improvement of safety and efficiency of the subway system is of great significance in ensuring the safety of passengers’ lives and property. Pedestrian gate-breaking behavior can not only cause equipment damage and traffic delays but also pose a threat to the safety of other passengers. Therefore, accurately detecting and recognizing the behavior of pedestrians breaking through subway gates has become an important task in intelligent transportation management. This study proposes a pedestrian gate-breaking threat detection algorithm. Firstly, the algorithm uses the mobile network convolution module in the feature extractor of the RAFT optical flow method and adds the ECA channel attention mechanism. At the same time, the 3D structure is used in the related volume building block and the field radius is reduced, to reduce the number of model parameters and improve the detection speed. Experimental results show that the average endpoint error of the proposed algorithm for pedestrian detection is 0.79. The detection speed can reach 55.98 f/s, and the number of model parameters is reduced by 35.3%. To obtain the threat value of passengers breaking through subway gates, this paper uses the improved optical flow method to calculate the motion information of adjacent picture frames and combines the gate-breaking threat calculation formula proposed in this study to obtain the threat value of passengers in the current picture frame. This method meets the requirements of real-time performance, accuracy, and lightweight design, and can be effectively deployed to better meet the engineering practice requirements of pedestrian threat detection and emergency management for large passenger flows within the station.