To　deal　with　the　clustering　problem　of　high-dimensional　complex　data,　it　is　usually　reguired　to　reduce　the　dimensionality　and　then　cluster,　but　the　common　dimensional　reduction　method　does　not　consider　the　clustering　characteristic　of　the　data　and　the　correlation　between　the　samples,　so　it　is　difficult　to　ensure　that　the　dimensional　reduction　method　matches　the　clustering　algorithm,　which　leads　to　the　loss　of　clustering　information.　The　nonlinear　unsupervised　dimensionality　reduction　method　extreme　learning　machine　autoencoder　(ELM-AE)　has　been　widely　used　in　dimensionality　reduction　and　denoising　in　recent　years　because　of　its　fast　learning　speed　and　good　generalization　performance.　In　order　to　maintain　the　original　subspace　structure　when　high-dimensional　data　is　projected　into　a　low-dimensional　space,　the　dimensional　reduction　method　ML-SELM-AE　is　proposed.　This　method　captures　the　deep　features　of　the　sample　set　by　using　the　multi-layer　extreme　learning　machine　autoencoder　while　maintaining　multi-subspace　structure　of　clustered　samples　by　self-representation　model.　Experimental　results　show　that　the　method　can　effectively　improve　the　clustering　accuracy　and　achieve　higher　learning　efficiency　on　UCI　data,　EEG　data　and　gene　expression　data.　Copyright　©2022　Acta　Automatica　Sinica.　All　rights　reserved.