While smart card data （SCD） collected from automatic fare collection （AFC） systems accurately records when and where people travel， they do not directly convey the trip purposes or activity types. In this study， we propose a method that integrates station clustering with an LDA model to uncover latent activities from urban rail transit passenger mobility data. First， we classified the stations into eight categories—employment，residential， mixed-use residential-employment， commercial centers， tourist attractions， composite hubs， external hubs， and ridership cultivation stations—using a constrained-seed K-means algorithm， based on demographic characteristics， ridership patterns， and the distribution of POIs around each station. Second， an LDA model is developed based on four key attributes： exit time， activity duration， origin station type， and destination station type. The model successfully identifies five primary activity types： shopping-related， work-related， home-related， tourism， and other. Furthermore， these patterns are divided into several subtopics， each distinguished by specific temporal and spatial characteristics， providing the theory support for deeply figuring out holiday travel patterns of urban rail transit passengers.