The　analysis　and　comprehension　of　multi-omics　data　has　emerged　as　a　prominent　topic　in　the　field　of　bioinformatics　and　data　science.　However,　the　sparsity　characteristics　and　high　dimensionality　of　omics　data　pose　difficulties　in　terms　of　extracting　meaningful　information.　Moreover,　the　heterogeneity　inherent　in　multiple　omics　sources　makes　the　effective　integration　of　multi-omics　data　challenging　To　tackle　these　challenges,　we　propose　MFCC-SAtt,　a　multi-level　feature　contrast　clustering　model　based　on　self-attention　to　extract　informative　features　from　multi-omics　data.　MFCC-SAtt　treats　each　omics　type　as　a　distinct　modality　and　employs　autoencoders　with　self-attention　for　each　modality　to　integrate　and　compress　their　respective　features　into　a　shared　feature　space.　By　utilizing　a　multi-level　feature　extraction　framework　along　with　incorporating　a　semantic　information　extractor,　we　mitigate　optimization　conflicts　arising　from　different　learning　objectives.　Additionally,　MFCC-SAtt　guides　deep　clustering　based　on　multi-level　features　which　further　enhances　the　quality　of　output　labels.　By　conducting　extensive　experiments　on　multi-omics　data,　we　have　validated　the　exceptional　performance　of　MFCC-SAtt.　For　instance,　in　a　pan-cancer　clustering　task,　MFCC-SAtt　achieved　an　accuracy　of　over　80.38%.