Traffic　flow　prediction　is　a　very　important　and　challenging　task　in　intelligent　transportation　systems.　There　has　been　a　lot　of　related　research　work　on　this　issue,　especially　the　application　of　graph　convolutional　networks　has　achieved　quite　good　results.　However,　the　existing　methods　usually　only　consider　the　temporal　and　spatial　dependence　in　traffic　data,　and　cannot　fully　explore　the　implicit　semantic　relationship　from　traffic　knowledge.　To　solve　this　problem,　we　model　the　transportation　system　as　topological　graphs　containing　different　types　of　knowledge　such　as　network　structure,　regional　functionality,　and　traffic　flow　patterns.　We　propose　a　Knowledge　Fusion　Enhanced　Graph　Neural　Network　(KFGNN)　module　based　on　multiple　graph　convolutional　networks.　Specifically,　topological　graphs　are　represented　by　relation　matrices　obtained　by　calculating　traffic　semantic　similarity,　and　are　used　as　the　input　of　the　Graph　Convolutional　Network(GCN)　layer　to　capture　the　semantic　dependence.　The　KFGNN　module　finally　fuses　these　features　to　obtain　a　complex　semantic　representation　of　the　traffic　flow.　Finally,　knowledge　fusion　enhanced　models　(KE-TGCN,　KE-STGCN　and　KE-GWN)　are　proposed　to　verify　the　effectiveness　and　versatility　of　this　module.　Experimental　results　on　real-world　datasets　show　that　knowledge-enhanced　models　have　higher　prediction　performance　compared　with　classic　GCN-based　models.　©　2023　Elsevier　B.V.