Existing　traffic　flow　prediction　methods　generally　only　consider　the　spatiotemporal　characteristics　of　traffic　flow.　However,　in　addition　to　the　spatiotemporal　characteristics,　the　interference　of　various　external　factors　needs　to　be　considered　in　traffic　flow　prediction,　including　severe　weather,　major　events,　traffic　control,　and　metro　failures.　The　current　research　still　cannot　fully　use　the　information　contained　in　these　external　factors.　To　address　this　issue,　we　propose　a　novel　metro　traffic　flow　prediction　method　(KGR-STGNN)　based　on　knowledge　graph　representation　learning.　We　construct　a　knowledge　graph　that　stores　factors　related　to　metro　traffic　networks.　Through　the　knowledge　graph　representation　learning　technology,　we　can　learn　the　influence　representation　of　external　factors　from　the　traffic　knowledge　graph,　which　can　better　incorporate　the　influence　of　external　factors　into　the　prediction　model　based　on　the　spatiotemporal　graph　neural　network.　Experimental　results　demonstrate　the　effectiveness　of　our　proposed　model.