Gait　recognition　is　a　biometric　technology　that　has　higher　accuracy　compared　to　fingerprint　recognition　and　facial　recognition.　Event　Camera　is　a　new　type　of　camera　with　advantages　such　as　high　temporal　resolution,　high　dynamic　range.　This　paper　proposes　a　hypergraph　neural　network　for　gait　recognition　based　on　event　camera.　By　using　event　flow　downsampling　module　for　downsampling,　the　amount　of　data　is　reduced　without　affecting　the　discriminability.　The　function　of　event　feature　extraction　module　is　to　convert　events　into　graph　nodes.　Spatiotemporal　hypergraph　convolution　module　can　construct　a　hypergraph,　extract　spatiotemporal　feature　and　obtain　pedestrian　gait　feature.　This　paper　constructs　a　gait　recognition　dataset　CASIA-B-EV　based　on　event　camera.　Finally,　the　effectiveness　of　the　proposed　model　is　verified　by　experiments.　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.