Multi-view　data　often　contain　redundant　information　that　cannot　be　simply　spliced.　Many　existing　methods　for　processing　them　by　assigning　weights　to　each　view　cannot　capture　features　dynamically.　Therefore,　we　propose　a　multi-view　deep　matrix　factorization　method　via　neural　networks　that　captures　semantic　hierarchical　information　of　the　data　and　dynamically　produces　a　consistent　representation　using　the　complementarity　of　multi-view　features.　Due　to　the　usefulness　of　deep　matrix　factorization,　the　generated　representation　is　easily　interpretable.　The　proposed　method　yields　a　harmonized　representation　directly　from　multi-view　data　without　an　extra　weight　learning　process.　In　addition,　we　use　a　multi-path　network　to　search　for　a　consensual　solution　and　obtain　an　optimal　result.　Additional　feature　optimization　is　used　to　enhance　the　discriminative　characterization　of　the　representation　matrix.　Finally,　experiments　on　four　real-world　datasets　show　that　the　proposed　method　is　superior　to　state-of-the-arts.　©　2022　IEEE.