User　electricity　data　contains　the　characteristics　of　residential　users＇　electricity　consumption　behavior.　In　order　to　help　power　companies　formulate　demand　response　plans　and　time　of　use　electricity　prices,　and　better　extract　electricity　consumption　behavior　characteristics,　this　paper　proposes　an　electricity　consumption　behavior　classification　model　based　on　sparse　denoising　autoencoder　(SDAE)　feature　dimensionality　reduction　and　K-means　clustering.　Firstly,　sparse　denoising　autoencoder　is　used　to　learn　features,　and　K-means　clustering　is　used　for　classification.　Visualize　the　classification　results　using　the　t-distributed　stochastic　neighbor　embedding　(t-SNE)　method,　calculate　typical　user　curves　using　Gaussian　distance　weighting,　and　analyze　the　characteristics　of　the　electricity　consumption　curve.　The　effectiveness　of　the　proposed　method　was　verified　by　calculating　and　comparing　the　clustering　indicators　of　other　common　dimensionality　reduction　methods.　©　2023　IEEE.