Gestures　recognition　based　on　surface　electromyography　(sEMG)　has　been　widely　used　for　human-computer　interaction.　However,　there　are　few　research　studies　on　overcoming　the　influence　of　physiological　factors　among　different　individuals.　In　this　paper,　a　cross-individual　gesture　recognition　method　based　on　long　short-term　memory　(LSTM)　networks　is　proposed,　named　cross-individual　LSTM　(CI-LSTM).　CI-LSTM　has　a　dual-network　structure,　including　a　gesture　recognition　module　and　an　individual　recognition　module.　By　designing　the　loss　function,　the　individual　information　recognition　module　assists　the　gesture　recognition　module　to　train,　which　tends　to　orthogonalize　the　gesture　features　and　individual　features　to　minimize　the　impact　of　individual　information　differences　on　gesture　recognition.　Through　cross-individual　gesture　recognition　experiments,　it　is　verified　that　compared　with　other　selected　algorithm　models,　the　recognition　accuracy　obtained　by　using　the　CI-LSTM　model　can　be　improved　by　an　average　of　9.15%.　Compared　with　other　models,　CI-LSTM　can　overcome　the　influence　of　individual　characteristics　and　complete　the　task　of　cross-individual　hand　gestures　recognition.　Based　on　the　proposed　model,　online　control　of　the　prosthetic　hand　is　realized.