The　development　of　electrical　measurement　technology　has　brought　high　latitude　residential　electricity　consumption　data　to　power　companies,　which　contains　the　characteristics　of　users＇　electricity　consumption　behavior　and　provides　data　support　for　the　behavior　classification.　In　order　to　improve　the　efficiency　of　data　feature　extraction　and　the　accuracy　of　electricity　consumption　behavior　identification,　a　classification　model　based　on　sparse　denoising　autoencoder　feature　dimensionality　reduction　and　spectral　clustering　is　proposed　in　this　paper.　Firstly,　the　sparse　denoising　autoencoder　(SDAE)　and　the　manually　defining　electricity　consumption　characteristic　indicators　are　deployed　to　extract　features　from　the　residential　daily　electricity　consumption　data,　and　then　the　spectral　clustering　is　employed　to　classify　the　extracted　electricity　consumption　characteristics.　Secondly,　the　t-distributed　stochastic　neighbor　embedding　(t-SNE)　is　applied　to　visualize　and　analyze　the　classification　results,　and　on　this　basis,　the　secondary　classification　is　implemented　to　fix　the　issue　of　the　confused　electricity　consumption　behaviors.　Finally,　the　typical　consumption　behavior　curves　are　calculated　by　Gaussian　distance　weighting　method,　and　the　characteristics　of　power　consumption　behavior　are　analyzed　and　summarized.　The　proposed　approach　is　evaluated　and　verified　by　using　the　electricity　dataset　in　Fujian,　China.　©　2024　The　Authors