Research　on　electroencephalogram-based　motor　imagery　(MI-EEG)　can　identify　the　limbs　of　subjects　that　generate　motor　imagination　by　decoding　EEG　signals,　which　is　an　important　issue　in　the　field　of　brain-computer　interface　(BCI).　Existing　deep-learning-based　classification　methods　have　not　been　able　to　entirely　employ　the　topological　information　among　brain　regions,　and　thus,　the　classification　performance　needs　further　improving.　In　this　paper,　we　propose　a　multi-view　graph　convolutional　attention　network　(MGCANet)　with　residual　learning　structure　for　multi-class　MI　decoding.　Specifically,　we　design　a　multi-view　graph　convolution　spatial　feature　extraction　method　based　on　the　topological　relationship　of　brain　regions　to　achieve　more　comprehensive　information　aggregation.　During　the　modeling,　we　build　an　adaptive　weight　fusion　(Awf)　module　to　adaptively　merge　feature　from　different　brain　views　to　improve　classification　accuracy.　In　addition,　the　self-attention　mechanism　is　introduced　for　feature　selection　to　expand　the　receptive　field　of　EEG　signals　to　global　dependence　and　enhance　the　expression　of　important　features.　The　proposed　model　is　experimentally　evaluated　on　two　public　MI　datasets　and　achieved　a　mean　accuracy　of　78.26%　(BCIC　IV　2a　dataset)　and　73.68%　(OpenBMI　dataset),　which　significantly　outperforms　representative　comparative　methods　in　classification　accuracy.　Comprehensive　experiment　results　verify　the　effectiveness　of　our　proposed　method,　which　can　provide　novel　perspectives　for　MI　decoding.