In　order　to　reduce　the　influence　of　the　change　of　muscle　contraction　force　on　EMG　pattern　recognition,　this　paper　proposes　the　feature　of　DCSP.　Firstly,　the　spatial　projection　matrix　that　maximizes　inter-class　distance　is　obtained　by　the　CSP.　Then,　the　new　signal　after　projection　is　differentiated　and　normalized.　Finally,　the　data　are　projected　into　the　low-dimensional　space　with　the　smallest　intra-class　distance　and　the　largest　inter-class　distance　by　the　uncorrelated　linear　discriminant　analysis,　the　EMG　gesture　based　on　DCSP　feature　is　verified　on　two　datasets.　The　first　dataset　contains　data　from　10　complete　limb　subjects,　and　the　second　dataset　contains　data　from　9　upper　limb　amputees.　Among　the　four　schemes　of　recognition　rate　testing,　the　recognition　accuracy　of　the　DCSP　feature　in　this　paper　is　higher　than　that　of　the　CSP　feature,　and　the　highest　recognition　rate　is　achieved　in　all　force　training　and　all　force　testing　schemes　(dataset1:　95.83%,　dataset2:　86.93%).　Compared　with　CSP　feature　(dataset1:89.01%,　dataset2:　70.03%),　the　classification　accuracy　rates　are　increased　by　6%　and　16%,　respectively.　In　the　two　test　schemes　of　feature　spatial　distribution,　the　DCSP　feature　has　a　smaller　intra-class　distance　and　a　larger　inter-class　distance　than　the　CSP　feature.　In　the　comparison　results　of　other　studies,　the　DCSP　feature　improves　the　recognition　accuracy　by　about　5%　(dataset1)　and　8%　(dataset2)　compared　with　the　existing　robust　features,　and　the　performance　does　not　depend　on　the　classifier.　©　2022,　Science　Press.　All　right　reserved.