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In the post-epidemic era, people's lives are gradually returning to normal, and travel is gradually resuming. The safe evacuation of cross-regional travelers in railway station has also attracted more and more attention, especially the evacuation behavior of college students in railway station. In this paper, considering the pedestrian dynamics mechanism in the emergency evacuation process during the COVID-19 normalized epidemic prevention and control, an Agent-based social force model was established to simulate the activities of college students in railway station. Combined with the virus infection transmission model, Monte Carlo simulation was used to calculate the total exposure time and the number of high-risk exposed people in the railway station evacuation process. In addition, sensitivity analysis was conducted on the total exposure time and the number of high-risk exposed people under 180 combinations of the number of initial infections, social distance, and the proportion of people wearing masks incorrectly. The results show that with the increase of social distances, the total exposure time and the number of high-risk exposures do not always decrease, but increase in some cases. The presence or absence of obstacles in the evacuation scene has no significant difference in the effects on total exposure time and the number of high-risk exposures. During the evacuation behavior of college students in railway station, choosing the appropriate number of lines can effectively reduce the total exposure time and the number of high-risk exposures. Finally, some policy suggestions are proposed to reduce the risk of virus transmission in the railway station evacuation process, such as choosing dynamic and reasonable social distance and the number of queues, and reducing obstacles. • Social force model and the virus transmission model are established to exposed the risk of virus transmission in the evacuation of college students in railway station using actual arrival data of students returning to school in Tianjin, China. • Influence parameters (the number of initial infections, social distance, and the proportion of people wearing masks incorrectly) and different scenarios (the presence or absence of obstacles and the number of lines) are analyzed to explore their influence on total exposure time and the number of high-risk exposures. • Relevant policies are proposed based on experimental results, including choosing dynamic and reasonable social distance and the number of queues, and reducing obstacles. [ABSTRACT FROM AUTHOR]