The　goal　of　multi-view　learning　is　to　learn　latent　patterns　from　various　data　sources.　Most　of　previous　research　focused　on　fitting　feature　embedding　in　target　tasks.　There　is　very　limited　research　on　the　connection　between　feature　representations　with　hidden　layers　of　neural　networks.　In　this　paper,　a　multi-view　deep　matrix　factorization　model　is　proposed　to　learn　a　shared　feature　representation.　The　proposed　model　automatically　explores　the　most　discriminative　features　of　multi-view　data　and　makes　these　features　meet　the　requirements　of　specific　applications.　Here　we　explore　the　connection　between　deep　learning　and　feature　representations.　First,　the　model　constructs　a　scalable　neural　network　with　shared　hidden　layers　for　exploring　a　low-dimensional　representations　of　all　views.　Second,　the　quality　of　representation　matrix　is　evaluated　via　relaxed　graph　regularization　and　evaluators　to　improve　the　feature　representation　capability　of　matrix　factorization.　Finally,　the　effectiveness　of　the　proposed　method　is　verified　through　comparative　experiments　with　eight　state-of-the-art　multi-view　clustering　algorithms　on　eight　real-world　datasets.