In response to the concept of land-use integration and creating micro-center around metro station in Beijing, this study extracted 23 quantitative indicators from public passenger flow, road network design, population density and land diversity to quantitatively analyze the built environment and travel characteristics of the non-motorized influence area based on multi-source big data. The connection characteristics of shared bicycles were taken into particular consideration. In order to compensate for the shortcomings of determining the influence range of metro stations by the traveler’s walking time, a classification model incorporating principal component analysis and K-means clustering was proposed to define the non-motorized influence area. Taking Beijing as an example, the study divided the metro stations into 4 clusters: inefficient connection-weak connectivity-residence oriented, efficient connection-high connectivity-balanced, efficient connection-weak connectivity-mixed, and efficient connection-high connectivity-work oriented. In order to verify the rationality of the clustering, the spatial auto-correlation was used to judge the spatial dependence of indicators. The results show that the spatial distributions of clusters 1, 3 and 4 do not differ significantly from the random model, while cluster 2 efficient connection-high connectivity-balanced stations has auto-correlation characteristics in space. Finally, based on the clustering results, the non-motorized influence areas of the metro stations were delineated as 2000, 1600, 1600, and 1700 m, respectively. The clarification of the non-motorized influence range of different metro station types can help urban planners determine the scope of micro-center construction and also lay the foundation for transportoriented development of urban in the future.