With　the　popularization　of　smartphones,　online　car-hailing　has　become　a　common　travel　alternative　and　plays　an　important　role　in　meeting　public　travel　demand.　Therefore,　online　car-hailing　operation　platforms　have　been　a　major　component　of　Intelligent　Transportation　Systems　in　which　passenger　demand　prediction　is　one　of　the　core　problems　to　be　solved.　However,　models　proposed　in　the　existing　literature　usually　ignore　the　long-term　temporal　correlation　and　multiple　spatial　correlations.　This　paper　presented　a　Spatio-Temporal　Multi-Graph　Convolutional　Network　Fused　With　Global　Features　(GST-MGCN)　to　address　the　limitations　of　existing　research　achievements,　taking　full　account　of　the　unique　spatiotemporal　correlations　of　the　travel　demand　of　online　car-hailing　passengers.　Following　the　Closeness,　Period,　and　Trend　(CPT)　paradigm,　the　model　fitted　temporal　dependencies　with　time　series　information.　By　identifying　multiple　spatial　semantic　correlations,　the　corresponding　relational　graph　structure　was　constructed,　and　a　multi-graph　convolutional　model　was　built　in　which　the　global　features　fusion　module　employed　gated　fusion　and　sum　fusion　methods　to　capture　sudden　and　gradual　changes　of　passenger　demand,　respectively.　Taking　the　Haikou　city　dataset　as　an　example,　our　experimental　results　show　that　the　values　of　the　three　indicators,　MAE,　RMSE,　and　MAPE　of　the　GST-MGCN　model　proposed　in　this　paper　were　2.269,　3.917,　and　21.447,　respectively,　which　were　lower　than　those　derived　from　other　similar　mainstream　models.　This　study　demonstrated　that　the　proposed　model　GST-MGCN　can　effectively　mine　the　spatio-temporal　pattern　of　online　car　hailing　passenger　travel　demand,　extract　the　impact　of　global　features,　and　accurately　predict　it.　©　2023　Journal　of　Geo-Information　Science.　All　rights　reserved.