Traffic　flow　prediction　plays　an　important　role　in　Intelligent　Transportation　Systems(ITS).　To　improve　the　accuracy　of　traffic　flow　prediction,　this　paper　proposes　a　multi-location　based　on　Trend-Seasonal　Decomposition　and　GCN　Traffic　Flow　Forecasting　Models　for　the　task　of　multi-location　traffic　flow　prediction.　In　this　paper,　the　proposed　model　mainly　consists　of　two　functions:　First,　the　Trend-Seasonal　component　decomposes　the　temporal　data　of　traffic　flow　into　a　more　predictable　trend　part　and　a　seasonal　or　periodic　part.　Second,　GCN　is　used　to　obtain　spatial　information　between　different　observation　points　and　improve　the　accuracy　of　multi-position　prediction.　Finally,　the　experiments　for　the　PeMS04　and　PeMS08　data　sets　are　carried　out　to　verify　the　effectiveness　of　proposed　model.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.