Multi-view　clustering　aims　to　improve　the　learning　performance　by　exploiting　discriminative　information　from　heterogeneous　data　sources.　It　has　been　capturing　growing　research　attention　due　to　its　wide　utilization　in　the　areas　of　data　mining　and　computer　vision.　However,　the　full　use　of　complementarity　and　consistency　from　multi-view　data　is　still　a　challenging　task.　Here,　we　propose　a　novel　multi-view　fuzzy　clustering,　which　adopts　the　joint　learning　of　deep　random　walk　and　sparse　low-rank　embedding.　First,　a　deep　random　walk　is　employed　to　acquire　a　robust　similarity　matrix　of　data　points　and　convert　fuzzy　membership　matrix　learning　to　adaptive　graph　learning.　Second,　the　adaptive　graph　is　restricted　with　sparse　low-rank　constraints,　which　ensures　its　strong　discriminative　ability　and　effective　cluster　assignments.　Third,　by　exploiting　the　sparse　low-rank　property,　the　multi-view　fuzzy　clustering　problem　is　formulated　as　optimizing　a　regularized　graph　adjacency　matrix　with　distance　metric　learning　and　spectral　norm　minimization　simultaneously.　The　distance　metric　learning　is　embedded　to　scatter　all　data　points　so　that　any　two　samples　are　projected　onto　a　low-dimensional　subspace　with　a　large　margin.　Finally,　an　efficient　algorithm　is　developed　for　the　formulated　problem　and　its　convergence　is　also　guaranteed.　In　order　to　demonstrate　the　superiority　of　the　proposed　method,　extensive　experiments　are　conducted　by　comparing　state-of-the-arts　on　real-world　databases.　©　2021　Elsevier　Inc.