As　a　single　learner,　extreme　learning　machine　autoencoder　(ELM-AE)　and　generalized　extreme　learning　machine　autoencoder　(GELM-AE)　have　limited　ability　to　learn　high-dimensional　complex　data　features　because　high-dimensional　data　contains　more　complex　and　rich　discriminative　information.　GELM-AE　only　pays　attention　to　the　internal　relationship　of　each　data　subset　for　dimensionality　reduction,　ignoring　the　relationship　between　different　subsets.　This　paper　proposes　the　homogeneous　ensemble　extreme　learning　machine　autoencoder　(HeELM-AE)　to　extract　high-dimensional　complex　data　diversity　features.　This　method　combines　the　ideas　of　ensemble　feature　learning　and　mutual　representation　matrix　learning.　Multiple　data　subsets　are　constructed　from　the　original　high-dimensional　complex　dataset　with　feature　learning　methods.　Generalized　extreme　learning　machine　autoencoder(GELM-AE)　is　used　as　a　base　dimension　reducer　to　learn　rich　discriminative　information　from　highly　redundant　features.　Mutual　representation　learning　methods　can　characterize　correlations　between　different　data　subsets　and　the　local　manifold　structure　inherent　in　different　data　subsets　is　maintained　through　manifold　regularization　at　the　same　time.　Extensive　comparative　experiments　on　medical　datasets　show　that　compared　with　other　ensemble　feature　learning　models,　HeELM-AE　is　an　efficient　and　accurate　model.　Finally,　visual　analysis　is　used　to　explain　the　working　mechanism　of　each　stage　of　HeELM-AE　and　explore　feature　learning　model　interpretability.