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At present, most stations in China still use video playback to identify abnormal behavior of passengers, with low efficiency and accuracy. In order to improve the intelligence of station passenger abnormal behavior monitoring, we adopt Convolutional Neural Network(CNN) algorithm, select the cross-border passengers in the platform area as the research object, and conduct intelligent recognition for the abnormal behavior to intelligently identify their abnormal behaviors. The Multi-Column Convolutional Neural Network(MCNN) algorithm is used to identify and monitor the crowd density in the waiting hall, ticket gate and other areas. Finally, we build a simulation platform, using the real video monitoring data to verify the models. With the help of simulation platform and station field data simulation verification, the results show that the number of people within the first and second boundaries of the platform accounts for about 12% of the total number of current video images, and the recognition rate is up to 90%. The results can provide operation guidance for the station safety guarantee business and passenger transport organization optimization, so as to ensure the safe and stable operation of the station.