Traffic　forecasting　is　a　challenging　problem　in　the　transportation　research　field　as　the　complexity　and　non-stationary　changing　of　the　traffic　data,　thus　the　key　to　the　issue　is　how　to　explore　proper　spatial　and　temporal　characteristics.　Based　on　this　thought,　many　creative　methods　have　been　proposed,　in　which　Graph　Convolution　Network　(GCN)　based　methods　have　shown　promising　performance.　However,　these　methods　depend　on　the　graph　construction,　which　mainly　uses　the　prior　knowledge　of　the　road　network.　Recently,　some　works　realized　the　fact　of　the　road　network　graph　changing　and　tried　to　construct　dynamic　graphs　for　GCN,　but　they　do　not　fully　exploit　the　spatial　and　temporal　properties　of　the　traffic　data　in　the　graph　construction.　In　this　paper,　we　propose　a　novel　dynamic　graph　convolution　network　for　traffic　forecasting,　in　which　a　latent　network　is　introduced　to　extract　spatial-temporal　features　for　constructing　the　dynamic　road　network　graph　matrices　adaptively.　The　proposed　method　is　evaluated　on　several　traffic　datasets　and　the　experimental　results　show　that　it　outperforms　the　state　of　the　art　traffic　forecasting　methods.　The　website　of　the　code　is　https://github.com/guokan987/DGCN.git.