The　surface　electromyography　signal　can　reflect　the　user＇s　action　intention.　Therefore,　it　becomes　the　main　control　signal　for　human-computer　interaction.　However,　the　individual　variability　makes　the　user　model　universally　un-applicable,　which　is　not　conducive　to　the　development　of　the　universal　EMG　equipment.　In　this　paper,　from　the　perspective　of　neural　synergy　control,　muscle　synergy　is　extracted　by　the　non-negative　matrix　factorization　algorithm.　Then,　the　pre-experimental　data　of　new　user　are　combined　with　least　squares　to　obtain　training　synergy　as　a　feature　quantity,　which　is　similar　to　pre-experimental　synergy.　For　application　consideration　in　low-frequency　wearable　scenarios,　three　simple　and　easily　portable　classifiers　(i.e.,　support　vector　machine,　error　back　propagation　network,　and　K-nearest　neighbor　algorithm)　are　trained　and　tested,　respectively.　Four　sets　of　gesture　recognition　experiments　are　implemented　in　DB1　(100　Hz)　and　DB5　(200　Hz)　of　the　Ninapro　database.　The　average　recognition　accuracy　rates　are　81.12%,　78.19%,　74.07%,　60.11%　(DB1)　and　85.75%,　83.25%,　79.07%,　66.10%　(DB5),　which　are　higher　than　the　existing　low-frequency　online　recognition　algorithms　by　more　than　10%.　The　proposed　algorithm　is　simple　and　easy　to　train　the　classifier　using　existing　user　data　and　a　small　amount　of　pre-experimental　data　from　new　users.　Meanwhile,　the　action　intention　can　be　judged　from　the　perspective　of　neural　coordination,　which　is　more　conducive　to　the　development　of　a　control　method　that　conforms　to　the　natural　movement　of　the　human　body.　It　provides　a　feasible　solution　for　the　popularization　of　wearable　electromyography　equipment.　©　2021,　Science　Press.　All　right　reserved.