Comprehensive　analysis　of　multi-omics　data　has　now　garnered　significant　attention.　However,　due　to　the　diversity　of　multi-omics　data,　integrating　multi-omics　information　presents　a　formidable　challenge　for　researchers.　Moreover,　the　high　dimensionality　and　sparsity　characteristics　in　omics　data　further　complicate　multi-omics　data　analysis.　To　address　these　challenges　and　obtain　high-quality　representations　suitable　for　downstream　tasks,　we　propose　a　self-supervised　clustering　learning　framework　called　the　Multi-view　Feature　Hierarchical　Contrastive　Clustering　model　(MFHCC)　to　extract　multi-level　features.　Firstly,　the　proposed　model　considers　multi-omics　as　multi-modality　and　employs　an　autoencoder　for　each　modality　to　integrate　diverse　omics　information　simultaneously.　Secondly,　it　utilizes　a　multilevel　feature　extraction　framework　with　contrastive　learning　methods　to　mitigate　the　impact　of　redundant　information　and　null　values　on　representation　quality　while　capturing　semantic　information　embedded　in　the　data.　Additionally,　the　model　incorporates　a　deep　clustering　module　to　guide　the　representation　toward　downstream　tasks　while　integrating　high-level　features　for　guidance.　Through　extensive　experiments　conducted　on　pan-cancer　datasets,　we　validate　the　effectiveness　of　MFHCC.　For　instance,　the　model　achieves　an　accuracy　exceeding　76%　by　omics　types,　thus　confirming　its　superior　performance.　©　2023　IEEE.