The　accuracy　of　the　regression　model　of　ridership　of　subway　stations　depends　on　the　scale　range　of　the　built　environment　around　the　subway　stations.　Previous　studies　have　not　considered　the　Modifiable　Area　Unit　Problem　(MAUP)　to　establish　the　regression　model　of　subway　station　ridership.　Taking　Beijing　as　an　example,　this　paper　expanded　the　built　environment　variables　from　＂5D＂　category　to　＂7D＂　category,　added　indicators　such　as　parking　fee　standard　and　population　density　factor,　and　proposed　a　Multi-Scale　Geographical　Weighted　Regression　(MGWR)　model　of　outbound　ridership　of　subway　stations　with　standardized　variables.　The　goodness　of　fit　of　regression　models　under　10　spatial　scales　or　built　environment　around　subway　stations　are　compared,　and　the　spatial　heterogeneity　of　built　environment　factors　under　the　optimal　spatial　scale　of　outbound　ridership　of　subway　stations　during　the　morning　peak　on　weekdays　is　discussed.　The　results　show　that:　(1)　the　scale　range　overlapped　by　1000　m　radius　circular　buffer　zone　and　Thiessen　polygon　has　the　highest　explanatory　power　for　the　regression　model,　and　is　regarded　as　the　optimal　scale　range　of　built　environment;　(2)　the　density　of　office　facilities,　sports　and　leisure　facilities,　medical　service　facilities,　building　density　and　floor　area　ratio　(FAR)　has　a　significant　impact　on　the　outbound　ridership　of　all　subway　stations;　(3)　office　facilities,　catering　facilities,　FAR,　number　of　parking　lots,　and　whether　subway　stations　are　transfer　stations　have　a　positive　impact　on　outbound　ridership.　The　number　of　medical　service　facilities,　sports　and　leisure　facilities,　bus　stops　and　building　density　have　a　negative　impact　on　outbound　ridership;　(4)　the　two　added　factors　in　this　study:　parking　charge　standard　and　population　density,　as　the　influencing　factors　of　the　built　environment,　have　a　significant　impact　on　the　outbound　ridership　of　some　subway　stations;　and　(5)　the　different　local　coefficients　of　the　built　environment　factors　at　different　stations　are　discussed,　which　indicate　the　spatial　heterogeneity　on　the　outbound　ridership.　The　results　can　provide　an　important　theoretical　basis　for　the　prediction　and　analysis　of　demand　of　ridership　at　subway　stations　and　the　integration　of　the　built　environment　around　the　stations.