Deep　multi-view　clustering　utilizes　neural　networks　to　extract　the　potential　peculiarities　of　complementarity　and　consistency　information　among　multi-view　features.　This　can　obtain　a　consistent　representation　that　improves　clustering　performance.　Although　a　multitude　of　deep　multi-view　clustering　approaches　have　been　proposed,　most　lack　theoretic　interpretability　while　maintaining　the　advantages　of　good　performance.　In　this　paper,　we　propose　an　effective　differentiable　network　with　alternating　iterative　optimization　for　multi-view　co-clustering　termed　differentiable　bi-sparse　multi-view　co-clustering　(DBMC)　and　an　extension　named　elevated　DBMC　(EDBMC).　The　proposed　methods　are　transformed　into　equivalent　deep　networks　based　on　the　constructed　objective　loss　functions.　They　have　the　advantages　of　strong　interpretability　of　the　classical　machine　learning　methods　and　the　superior　performance　of　deep　networks.　Moreover,　DBMC　and　EDBMC　can　learn　a　joint　and　consistent　collaborative　representation　from　multi-source　features　and　guarantee　sparsity　between　multi-view　feature　space　and　single-view　sample　space.　Meanwhile,　they　can　be　converted　into　deep　differentiable　network　frameworks　with　block-wise　iterative　training.　Correspondingly,　we　design　two　three-step　iterative　differentiable　networks　to　resolve　resultant　optimization　problems　with　theoretically　guaranteed　convergence.　Extensive　experiments　on　six　multi-view　benchmark　datasets　demonstrate　that　the　proposed　frameworks　outperform　other　state-of-the-art　multi-view　clustering　methods.