Network　traffic　prediction　is　essential　and　significant　to　network　management　and　network　security.　Existing　prediction　methods　cannot　well　capture　the　temporal-spatial　correlations　hidden　in　the　network　traffic　and　suffer　from　a　prediction　accuracy　degradation　for　two　reasons.　First,　existing　approaches　concentrate　on　capturing　the　spatial　relations　and　dependence　in　an　explicit　network　topology　graph,　which　fails　to　reflect　the　inherent　relations　in　network　traffic.　Second,　the　common　recurrent　neural　network　models　which　are　leveraged　to　learn　the　temporal　relations　in　network　traffic　exhibit　a　poor　performance　in　long-term　prediction　for　its　limited　receptive　field.　To　tackle　these　two　problems,　we　propose　an　Attention-based　Graph　Convolutional　Network　model　(AGCN)　for　capturing　both　the　spatial　and　temporal　correlations　in　network　traffic.　To　catch　the　hidden　spatial　dependencies　in　network　traffic,　we　combine　graph　attention　network　with　graph　convolutional　network　to　mine　the　spatial　relationships　of　network　traffic.　To　efficiently　learn　the　temporal　long-term　relations　embedded　in　network　traffic,　we　design　a　dilated　convolution　module　to　enable　an　exponentially　growing　receptive　field　for　handling　long　sequences.　Experimental　results　on　three　network　traffic　datasets　show　that　AGCN　has　excellent　performance　in　terms　of　prediction　accuracy　and　inference　time　compared　to　current　mainstream　methods.　©　2023　IEEE.