The　surface　electromyography　(sEMG)　is　one　of　the　basic　processing　techniques　to　the　gesture　recognition　because　of　its　inherent　advantages　of　easy　collection　and　non-invasion.　However,　limited　by　feature　extraction　and　classifier　selection,　the　adaptability　and　accuracy　of　the　conventional　machine　learning　still　need　to　promote　with　the　increase　of　the　input　dimension　and　the　number　of　output　classifications.　Moreover,　due　to　the　different　characteristics　of　sEMG　data　and　image　data,　the　conventional　convolutional　neural　network　(CNN)　have　yet　to　fit　sEMG　signals.　In　this　paper,　a　novel　hybrid　model　combining　CNN　with　the　graph　convolutional　network　(GCN)　was　constructed　to　improve　the　performance　of　the　gesture　recognition.　Based　on　the　characteristics　of　sEMG　signal,　GCN　was　introduced　into　the　model　through　a　joint　voting　network　to　extract　the　muscle　synergy　feature　of　the　sEMG　signal.　Such　strategy　optimizes　the　structure　and　convolution　kernel　parameters　of　the　residual　network　(ResNet)　with　the　classification　accuracy　on　the　NinaPro　DBl　up　to　90.07%.　The　experimental　results　and　comparisons　confirm　the　superiority　of　the　proposed　hybrid　model　for　gesture　recognition　from　the　sEMG　signals.　©　2023　Beijing　Institute　of　Technology.　All　rights　reserved.