Accurate　traffic　flow　prediction　can　improve　urban　commuting　efficiency,　but　how　to　effectively　mine　the　spatio-temporal　characteristics　of　traffic　data　is　the　biggest　challenge.　Therefore,　this　paper　proposes　a　spatio-temporal　convolution　method,　which　can　effectively　extract　the　traffic　dynamic　correlation　of　different　road　nodes,　and　reconstruct　the　road　network　by　cosine　similarity　principle.　This　principle　can　reflect　the　similar　situation　of　traffic　change　trend　of　two　road　nodes　in　the　past　period　of　time.　In　order　to　sample　the　time-dependent　features　of　road　nodes,　this　study　uses　a　time-dependent　convolutional　network,　which　has　the　ability　to　extract　sequence　time-dependent　features　from　longer　data.　　©　2023　IEEE.