With　the　deterioration　of　the　transportation　ecosystem　and　traffic　congestion,　traffic　graph　representations　become　more　complex　and　lack　intelligibility.　The　deep　learning　method　provides　a　new　way　for　traffic　prediction　by　mining　the　spatiotemporal　relations　of　historical　states　in　traffic　network.　Recurrent　neural　networks　based　on　gate　control　can　avoid　the　gradient　vanishing　and　graph　convolutional　networks　provide　theoretical　support　for　spatial　feature　extraction　of　traffic　graph.　However,　in　the　prediction　of　traffic　evolution　behaviors,　the　statuses　of　units　and　groups　distributed　in　the　traffic　network　have　strong　and　continuous　spatiotemporal　correlations.　Modeling　only　based　on　the　temporal　or　spatial　perspective　usually　lacks　comprehensiveness　and　semantic　relevance　which　results　in　poor　prediction　performance.　In　this　paper,　we　propose　a　spatiotemporal　hierarchical　propagation　graph　convolutional　network(SHPGCN)　to　get　the　whole　spatiotemporal　evolution　features　of　traffic　graph,　which　can　be　used　to　predict　the　propagation　effects　and　state　changes　of　traffic　flow.　Considered　the　inadequacy　of　graph　convolution　in　the　learning　of　hierarchical　features,　we　construct　a　low-dimensional　propagation　graph　representation　that　projects　complex　node　relationships　into　first-order　neighborhoods　to　capture　dynamic　changes　at　different　spatial　and　temporal　scales.　The　SHPGCN　uses　graph　convolution　to　encode　the　traffic　propagation　features　and　embeds　them　into　a　bidirectional　recurrent　sequence　for　traffic　prediction.　The　experiment　results　show　that　SHPGCN　can　provide　good　prediction　accuracy　and　robustness　for　the　traffic　evolution　behaviors.