In　order　to　address　the　problem　of　air　pollution　along　with　the　fast　development　of　the　economy　and　an　increase　of　urban　population,　this　paper　aims　to　capture　the　spatial–temporal　correlation　in　air　quality　data　and　improve　the　prediction　accuracy　of　PM2.5　concentration.　Existing　models　usually　neglect　the　correlation　in　temporal　and　spatial　dimensions,　and　the　prediction　results　do　not　correspond　to　those　in　practical　applications.　We　propose　a　multi-period　spatial–temporal　graph　convolutional　network　(MST-GCN),　in　which　the　time　division　module　fully　explores　the　temporal　correlation　within　the　data　and　builds　separate　models　for　different　time　periods;　the　spatial–temporal　convolution　module　captures　the　characteristics　of　historical　data　in　the　time　dimension　and　spatial　dimension,　in　which　GCN　is　used　to　learn　spatial　dependencies　between　stations,　and　CNN　is　applied　to　learn　the　temporal　dependencies　of　adjacent　time　points;　meanwhile,　a　spatial–temporal　attention　mechanism　module　is　introduced　to　enhance　the　key　information　in　different　dimensions,　thereby　capturing　the　spatial–temporal　dynamic　correlation.　Finally,　the　experiments　with　real　air　quality　data　from　Beijing　monitoring　stations　show　that　the　time　division　method　is　effective　to　extract　the　features　in　time　dimensions;　therefore,　the　model　is　able　to　capture　the　characteristics　and　dynamic　correlation　of　air　quality　data　in　spatial–temporal　dimensions.　The　superiority　of　the　proposed　method　has　been　validated　over　state-of-the-art　methods,　and　the　average　mean　absolute　error　and　root-mean-square　error　of　MST-GCN　are　34.37　and　35.55,　respectively,　which　achieves　the　best　performance　in　the　experiments.　©　2023,　The　Author(s)　under　exclusive　licence　to　Iranian　Society　of　Environmentalists　(IRSEN)　and　Science　and　Research　Branch,　Islamic　Azad　University.