With　the　development　of　society　and　economy,　the　urban　rail　transit　has　become　one　of　the　important　components　of　urban　transportation　system,　while　the　construction　of　the　urban　rail　greatly　improves　the　public　transportation　environments.　Currently,　there　are　many　research　focus　on　the　passenger　flow　predictions　according　to　their　corresponding　historical　data,　however,　it　is　hard　to　assist　transport　models　vary　such　volumes　for　a　new　station　planning　or　being　constructed.　In　view　of　this　limitation,　we　provide　a　novel　method　for　urban　rail　station　characteristics　analysis　in　intelligent　transportation　considering　city　land　usages.　Initially,　point　of　interest　(POIs)　are　divided　by　the　proposed　RC-tree　(Colored　R-tree)-based　algorithm　into　the　bounded　areas　for　each　station.　Second,　the　Diversity　and　Proportion　approaches　are　proposed　to　extract　the　top-k　POIs　from　bounded　areas　based　on　their　semantic　and　spatial　characteristics.　Then,　classify　the　stations　based　on　the　similarity　of　the　extracted　top-k　POIs.　Moreover,　we　made　a　case　study　on　real　dataset,　including　a　large　volume　of　Automatic　Fare　Collection　system　(AFC)　records　for　the　experimental　evaluations,　and　the　results　show　that　the　proposed　method　can　verify　the　rationality　of　land　use　and　provide　support　for　the　application　of　transportation　model　technology.