Multi-view　clustering　has　attracted　increasing　attention　by　reason　of　its　ability　to　leverage　the　complementarity　of　multi-view　data.　Existing　multi-view　clustering　methods　have　explored　nonnegative　matrix　factorization　to　decompose　a　matrix　into　multiple　matrices　for　feature　representations　from　multi-view　data,　which　are　not　discriminative　enough　to　deal　with　the　natural　data　containing　complex　information.　Moreover,　most　of　multi-view　clustering　methods　prioritize　the　consensus　information　among　multi-view　data,　leaving　a　large　amount　of　information　redundant　and　the　clustering　performance　deteriorated.　To　address　these　issues,　this　paper　proposes　a　multi-view　clustering　framework　that　adopts　a　diversity　loss　for　deep　matrix　factorization　and　reduces　feature　redundancy　while　obtaining　more　discriminative　features.　We　then　bridge　the　relation　between　deep　auto-encoder　and　deep　matrix　factorization　to　optimize　the　objective　function.　This　method　avoids　the　challenges　in　the　optimization　process.　Extensive　experiments　demonstrate　that　the　proposed　method　is　superior　to　state-of-the-art　methods.　©　2022　Elsevier　Inc.