In　motor　imagery　tasks,　the　brain　often　involves　simultaneous　activation　of　multiple　regions,　and　traditional　convolutional　neural　networks　struggle　to　accurately　represent　the　coordinated　neural　activity　across　these　regions.　Graph　convolutional　network　GCN　is　suitable　for　representing　the　collaborative　tasks　of　different　brain　regions　by　considering　the　connections　and　relationships　between　nodes　(brain　regions)　in　graph　data.　Attention-fused　filter　bank　dual-view　GCN（　AFB-DVGCN）　was　proposed.　A　dual-branch　network　was　constructed　using　filter　banks　to　extract　temporal　and　spatial　information　from　different　frequency　bands.　Information　complementarity　was　achieved　by　a　convolutional　spatial　feature　extraction　method　for　dual-view　graphs.　In　order　to　improve　the　classification　accuracy,　the　effective　channel　attention　mechanism　was　utilized　to　enhance　features　and　capture　the　interaction　information　between　different　feature　maps.　Validation　results　in　the　publicly　available　datasets　BCI　Competition　IV-2a　and　OpenBMI　show　that　AFB-DVGCN　has　achieved　good　classification　performance,　and　the　classification　accuracy　is　significantly　higher　than　that　of　the　comparison　networks.　©　2024　Zhejiang　University.　All　rights　reserved.