Network　traffic　prediction　plays　a　significant　role　in　network　management.　Previous　network　traffic　prediction　methods　mainly　focus　on　the　temporal　relationship　between　network　traffic,　and　used　time　series　models　to　predict　network　traffic,　ignoring　the　spatial　information　contained　in　traffic　data.　Therefore,　the　prediction　accuracy　is　limited,　especially　in　long-term　prediction.　To　improve　the　prediction　accuracy　of　the　dynamic　network　traffic　in　the　long　term,　we　propose　an　Attention-based　Spatial–Temporal　Graph　Network　(ASTGN)　model　for　network　traffic　prediction　to　better　capture　both　the　temporal　and　spatial　relations　between　the　network　traffic.　Specifically,　in　ASTGN,　we　exploit　an　encoder–decoder　architecture,　where　the　encoder　encodes　the　input　network　traffic　and　the　decoder　outputs　the　predicted　network　traffic　sequences,　integrating　the　temporal　and　spatial　information　of　the　network　traffic　data　through　the　Spatio-Temporal　Embedding　module.　The　experimental　results　demonstrate　the　superiority　of　our　proposed　method　ASTGN　in　long-term　prediction.　©　2024　Elsevier　B.V.