With　the　development　of　deep　learning　technology,　gesture　recognition　based　on　surface　electromyography　(EMG)　signals　has　shown　broad　application　prospects　in　various　human-computer　interaction　fields.　Most　current　gesture　recognition　technologies　can　achieve　high　recognition　accuracy　on　a　wide　range　of　gesture　actions.　However,　in　practical　applications,　gesture　recognition　based　on　surface　EMG　signals　is　susceptible　to　interference　from　irrelevant　gesture　movements,　which　affects　the　accuracy　and　security　of　the　system.　Therefore,　it　is　crucial　to　design　an　irrelevant　gesture　recognition　method.　This　paper　introduces　the　GANomaly　network　from　the　field　of　image　anomaly　detection　into　surface　EMG-based　irrelevant　gesture　recognition.　The　network　has　a　small　feature　reconstruction　error　for　target　samples　and　a　large　feature　reconstruction　error　for　irrelevant　samples.　By　comparing　the　relationship　between　the　feature　reconstruction　error　and　the　predefined　threshold,　we　can　determine　whether　the　input　samples　are　from　the　target　category　or　the　irrelevant　category.　In　order　to　improve　the　performance　of　EMG　irrelevant　gesture　recognition,　this　paper　proposes　a　feature　reconstruction　network　named　EMG-FRNet　for　EMG　irrelevant　gesture　recognition.　This　network　is　based　on　GANomaly　and　incorporates　structures　such　as　channel　cropping　(CC),　cross-layer　encoding　decoding　feature　fusion　(CLEDFF),　and　SE　channel　attention　(SE).　In　this　paper,　Ninapro　DB1,　Ninapro　DB5　and　self-collected　datasets　were　used　to　verify　the　performance　of　the　proposed　model.　The　Area　Under　the　receiver　operating　characteristic　Curve　(AUC)　values　of　EMG-FRNet　on　the　above　three　datasets　were　0.940,　0.926　and　0.962,　respectively.　Experimental　results　demonstrate　that　the　proposed　model　achieves　the　highest　accuracy　among　related　research.