Air　pollution　is　a　serious　threat　to　both　the　ecological　environment　and　the　physical　health　of　individuals.　Therefore,　accurate　air　quality　prediction　is　urgent　and　necessary　for　pollution　mitigation　and　residents＇　travel.　However,　few　existing　models　are　established　based　on　the　dynamic　spatiotemporal　correlation　of　air　pollutants　to　predict　air　quality.　In　this　paper,　a　novel　deep　learning　model　combining　the　dynamic　graph　convolutional　network　and　the　multi-channel　temporal　convolutional　network　(DGC-MTCN)　is　proposed　for　air　quality　pre-diction.　To　efficiently　represent　the　time-varying　spatial　dependencies,　a　new　spatiotemporal　dynamic　correla-tion　calculation　method　based　on　gray　relation　analysis　is　proposed　to　construct　dynamic　adjacency　matrices.　Then,　the　spatiotemporal　features　are　sufficiently　extracted　by　the　graph　convolutional　network　and　the　multi-channel　temporal　convolutional　network.　Two　real-world　air　quality　datasets　collected　from　Beijing　and　Fushun　are　applied　to　verify　the　performance　of　our　proposed　model.　The　experimental　results　show　that　compared　with　other　baselines,　the　DGC-MTCN　model　has　excellent　prediction　accuracy.　Especially　for　the　prediction　of　multi-step　and　different　stations,　our　model　performs　better　temporal　stability　and　generalization　ability.