Rail transportation plays a crucial role in improving travel efficiency and reducing traffic congestion. Owing to the concentration of resources, Dongcheng and Xicheng districts in Beijing City face serious traffic congestion. Therefore, rail transportation has become essential for daily commuting in such a region. The convenience and user-friendliness of slow mobility systems around rail stations affect the travel experience and efficiency. It is also an important aspect of the current urban renewal on rail transportation upgrades in Beijing. Therefore, in this study, we focus on evaluating the last mile problems on slow mobility systems around rail stations for Dongcheng and Xicheng districts. We utilize internet and spatial big data to establish a slow mobility evaluation system, including indicators such as station vitality, population coverage, surrounding environment, accessibility, and detour coefficient by using the Python programming language, Feature Manipulation Engine (FME), and Geographic Information System (GIS) network analysis methods.