Traffic　prediction　is　an　important　part　of　intelligent　transportation　system.　Recently,　graph　convolution　network　(GCN)　is　introduced　for　traffic　flow　forecasting　and　achieves　good　performance　due　to　its　superiority　of　representing　the　graph　traffic　road　structure　network.　Moreover,　the　dynamic　GCN　is　put　forward　to　model　the　temporal　property　of　the　traffic　flow.　Although　great　progress　has　been　made,　most　GCN　based　traffic　flow　forecasting　methods　utilize　a　single　graph　for　convolution,　which　is　considered　not　enough　to　reveal　the　inherent　property　of　traffic　graph　as　it　is　influenced　by　many　factors,　for　example　weather,　season　and　traffic　accidents　etc.　In　this　paper,　an　exotic　graph　transformer　based　dynamic　multiple　graph　convolution　networks　(GTDMGCN)　is　conceived　for　traffic　flow　forecasting.　Instead　of　the　single　graph,　multiple　graphs　are　constructed　to　modulate　the　complex　traffic　network　by　the　proposed　graph　transformer　network.　Additionally,　a　temporal　gate　convolution　is　proposed　to　get　the　temporal　property　of　traffic　flow.　The　proposed　GTDMGCN　model　is　evaluated　on　four　real　traffic　datasets　of　PEMS03,　PEMS04,　PEMS07,　PEMS08,　and　there　are　average　increments　of　9.78%,　7.80%,　5.96%　under　MAE,　RMSE,　and　MAPE　metrics　compared　with　the　current　results.