In　recent　years,　deep　multi-view　representation　learning　has　made　considerable　achievements　due　to　its　excellent　nonlinear　mapping　capability.　Yet　its　development　is　limited　by　the　challenge　of　interpreting　the　underlying　structure.　For　this　reason,　we　propose　a　differentiable　multi-view　representation　learning　network　to　address　the　aforementioned　issue,　which　is　equipped　with　the　interpretable　working　mechanism　of　sparse　low-rank　decomposition　and　outstanding　representation　ability　of　neural　networks.　The　network　is　constructed　by　stacking　multiple　differentiable　blocks　that　are　explicitly　reformulated　from　the　optimization　objective　exhibiting　interpretability.　Benefiting　from　end-to-end　optimization　of　deep　networks,　it　can　efficiently　learn　an　interpretable　deep　representation　of　high-dimensional　features　from　multi-view　data.　Extensive　experimental　results　on　several　benchmark　multi-view　datasets　demonstrate　the　effectiveness　of　the　learned　representation　in　comparison　to　several　state-of-the-art　algorithms.