Accurate　and　efficient　short-term　traffic　flow　prediction　is　one　of　the　basic　guarantees　for　solving　various　traffic　problems.　According　to　the　road　network　topology　and　the　temporal　and　spatial　correlation　of　traffic　flow,　a　multi-step　road　network　speed　prediction　method　based　on　graph　convolution-long　short-term　memory　neural　network　(GCLSTM)　is　proposed.　Firstly,　the　weight　parameters　of　the　convolutional　layer　are　designed　to　increase　the　connection　between　the　convolutional　layers.　Convolutional　layer　is　embedded　in　the　gating　unit　of　long　short-term　memory　neural　network　(LSTM)　to　achieve　the　effect　of　capturing　the　spatial　and　temporal　correlation　at　the　same　time.　Secondly,　an　encoder　and　decoder　structure　is　used　to　realize　multi-step　prediction.　The　structure　gradually　increase　the　output　sequence　length　to　improve　the　efficiency　of　model　training.　Finally,　ablation　experiments　and　comparison　experiments　are　performed　on　the　expressway　dataset　and　the　urban　road　dataset.　The　ablation　experiments　show　that　the　error　index　of　the　hybrid　network　prediction　model　is　significantly　reduced.　The　comparative　experiments　exhibit　that　the　GCLSTM　prediction　model　has　higher　multi-step　prediction　accuracy　and　better　prediction　performance　at　a　single　observation　point.　This　model　provides　accurate　prediction　information　for　intelligent　transportation.