In　order　to　solve　the　problem　of　great　effect　by　weather　conditions　and　the　difficulty　in　extracting　the　temporal　and　spatial　features　of　delay　in　the　process　of　airport　delay　prediction,　a　meteorology-based　spatio-temporal　graph　convolutional　networks　(MSTGCN)　model　is　proposed　in　this　paper.　This　model　uses　graph　convolutional　neural　network　(GCNN)　and　gated　convolutional　neural　network　(Gated　CNN)　to　extract　the　temporal　and　spatial　features　of　airport　delays,　and　integrates　meteorological　characteristics　extracting　module　to　predict　delay　time　of　airports.　The　experiment　results　show　that　the　performance　of　the　proposed　model　in　medium　and　short　term　prediction　is　superior　to　other　comparative　models.　Compared　with　the　model　that　does　not　consider　meteorological　factors,　the　mean　absolute　errors　of　MSTGCN　for　the　next　1　h,　4　h　and　12　h　are　respectively　decreased　by　7.　03%,　7.　93%,　11.　54%,　which　greatly　revises　the　prediction　results.　©　2023　Chinese　Institute　of　Electronics.　All　rights　reserved.