The　pattern　recognition　of　surface　electromyography　(sEMG)　signal　is　an　important　application　in　the　realization　of　human-machine　interface.　However,　due　to　the　disturbance　of　human　body,　sensors　and　environment,　sEMG　signal　usually　contains　lots　of　noise,　which　brings　great　challenges　to　the　high-accuracy　sEMG　pattern　recognition.　In　addition,　embedded　human　wearable　devices　are　becoming　more　and　more　popular　nowadays.　How　to　realize　the　sEMG　recognition　method　with　low　power　consumption　and　high　noise　immunity　has　also　become　a　difficult　and　very　meaningful　research　topic.　In　this　paper,　a　spiking　neural　network　(SNN)　classification　method　based　on　second-order　information　bottleneck　training　is　proposed.　Firstly,　the　training　loss　function　for　classification　neural　networks　is　constructed　based　on　the　proposed　second-order　information　bottleneck.　The　method　is　used　to　train　the　conventional　continuous-valued　neural　network　and　convert　it　into　an　SNN　model　with　equivalent　structure　and　connection　weights.　Then,　the　converted　SNN　is　used　to　classify　the　sEMG　signal　patterns.　Through　a　series　of　theoretical　analysis　and　experimental　results,　it　is　proved　that　this　method　has　significant　advantages　in　terms　of　generalization　of　network　determination　and　computational　efficiency.　The　experimental　code　can　be　accessed　from　https://github.com/anvien/2OIB-for-sEMG-Recognition.　©　2021