A　short-term　traffic　flow　forecasting　model　combining　graph　convolution　network　(GCN)　and　bidirectional　long-term　memory　neural　network　(BiLSTM)　was　proposed.　The　topological　structure　of　road　network　was　extracted　by　graph　convolution　network　to　solve　the　topological　relationship　problem　and　extract　the　spatial　characteristics　between　road　networks.　The　bidirectional　long-term　and　short-term　memory　neural　network　was　used　to　learn　the　dynamic　changes　of　traffic　data　to　obtain　the　time　correlation,　and　the　GCN-BiLSTM　model　was　fused　to　realize　the　traffic　flow　prediction　considering　the　time-space　relationship　of　the　road　network.　The　results　show　that　the　method　proposed　in　this　paper　can　better　adapt　to　the　traffic　flow　under　different　traffic　flow　characteristics,　and　the　prediction　deviation　of　working　days　and　weekends　is　reduced　by　12.　24%　and　13.　20%　compared　with　the　classical　algorithm.　©　2023　Wuhan　University　of　Technology.　All　rights　reserved.