The　metro　system　is　indispensable　for　alleviating　traffic　congestion　in　the　urban　transportation　system.　Precise　metro　passenger　flow　(MPF)　prediction　is　crucial　in　ensuring　smooth　operations　of　the　metro　system.　Recently,　the　graph　convolutional　network　(GCN),　which　is　effective　in　the　spatial　feature　extraction,　has　been　applied　in　traffic　prediction.　However,　most　existing　GCN-based　methods　construct　the　empirical　graphs　based　on　distance　and　adjacency,　which　cannot　fully　express　the　correlations　of　metro　stations.　This　paper　proposes　a　novel　MPF　prediction　method　consisting　of　three　parts:　K　-means　-based　metro　station　functional　clustering　(KMSFC),　external　feature　fusion,　and　dual　-view　recurrent　GCN　(DVRGCN).　The　KMSFC　identifies　the　metro　stations　both　having　similar　MPF　changing　tendencies　and　being　located　in　similar　urban　functional　areas.　Furthermore,　the　DVRGCN　is　designed　to　simultaneously　capture　the　spatiotemporal　and　external　features.　The　dual　-view　GCN　module　in　the　DVRGCN　captures　both　explicit　and　implicit　spatial　features　of　the　metro　traffic　network.　To　demonstrate　the　capability　for　making　accurate　MPF　predictions,　the　experiments　using　a　real　-world　metro　traffic　dataset　are　conducted.　The　ablation　experiments　are　also　performed　to　prove　the　contribution　of　each　module　in　the　proposed　method.　The　experimental　results　show　that　the　proposed　method　outperforms　other　state-of-the-art　traffic　prediction　methods.