The　advent　of　data　augmentation　attracts　a　lot　of　attention　on　how　to　handle　unstructured　data.　Data　augmentation　makes　it　easier　to　mine　the　essential　data　to　extract　more　useful　information.　And　it　benefits　the　neural　network＇s　capacity　for　generalization.　Numerous　studies　contradict　our　perspective　on　data　augmentation　and　a　part　of　them　are　mature　enough.　However,　relative　studies　on　spiking　neural　networks　still　have　the　potential　to　grow　and　develop　due　to　the　properties　of　SNN.　To　achieve　deeper　neural　networks　and　modify　the　data　augmentation,　the　primary　challenge　is　how　to　adjust　data　variances　within　a　normal　range.　CutMix　is　a　member　of　data　augmentation.　It　adopts　the　idea　of　regional　dropout　and　patches　cropped　images　on　original　images.　Owing　to　the　rules　of　neurons,　SNN　has　a　single　activation　value.　So　lost　information　is　an　important　issue　that　we　must　address.　This　shows　that　regional　dropout　with　rules　of　neurons　may　sharpen　the　problem　of　lost　information.　So　we　propose　a　method　to　improve　the　performance　of　SNN　through　CutMix.　The　accuracy　of　SNN　on　CIFAR-10　is　1.39%　and　0.95%　higher　than　vanilla　ANNs(with　ZCA　pre-processing)　and　ANNs(with　moderate　data　augmentation　and　mean/std　normalization).　And　the　accuracy　of　SNN　on　Fashion-MNIST　is　0.9%　higher　than　ANN.　The　work　of　the　paper　is　of　great　significance　to　developing　deeper　SNN.　©　2023　IEEE.