Abstract: With the continuous improvement of railway transportation capacity, the frequency and time of passengers waiting for trains in railway passenger stations are also increasing. To actively explore the personalized needs of waiting passengers, this paper proposed a passenger attribute recognition method for railway passenger station based on the AL-Transformer (Attribute Localization Transformer) model. The paper used AL-Transformer model based on the Swin Transformer backbone network to extract structured information of passengers entering the stations, suppressed feature region correlation through the Mask Contrast Learning (MCL) framework to obtain more recognizable attribute regions, and used Attribute Spatial Memory (ASM) module to selecte more reliable and stable attribute related regions. The trial results at Baiyin South Station of CHINA RAILWAY Lanzhou Group show that this method can effectively identify passenger attributes, push more targeted information for station staff, improve the quality of passenger service at the station, and ensure the safety of passenger waiting.