Multi-view　clustering　on　traditional　optimization　methods　is　derived　from　different　theoretical　frameworks,　yet　it　may　be　inefficient　in　dealing　with　complex　multi-view　data　compared　to　deep　models.　In　contrast,　deep　multi-view　clustering　methods　for　implicit　optimization　have　excellent　feature　abstraction　ability　but　are　inscrutable　due　to　their　black-box　problem.　However,　very　limited　research　was　devoted　to　integrating　the　advantages　of　the　above　two　types　of　methods　to　design　an　efficient　method　for　multi-view　clustering.　Focusing　on　these　problems,　this　paper　proposes　a　differentiable　bi-level　optimization　network　(DBO-Net)　for　multi-view　clustering,　which　is　implemented　by　incorporating　the　traditional　optimization　method　with　deep　learning　to　design　an　interpretable　deep　network.　To　enhance　the　representation　capability,　the　proposed　DBO-Net　is　constructed　by　stacking　multiple　explicit　differentiable　block　networks　to　learn　an　interpretable　consistent　representation.　Then　all　the　learned　parameters　can　be　implicitly　optimized　through　back-propagation,　making　the　learned　representation　more　suitable　for　the　clustering　task.　Extensive　experimental　results　validate　that　the　strategy　of　bi-level　optimization　can　effectively　improve　clustering　performance　and　the　proposed　method　is　superior　to　the　state-of-the-art　clustering　methods.