Multiview　subspace　clustering　has　been　demonstrated　to　achieve　excellent　performance　in　practice　by　exploiting　multiview　complementary　information.　One　of　the　strategies　used　in　most　existing　methods　is　to　learn　a　shared　self-expressiveness　coefficient　matrix　for　all　the　view　data.　Different　from　such　a　strategy,　this　article　proposes　a　rank　consistency　induced　multiview　subspace　clustering　model　to　pursue　a　consistent　low-rank　structure　among　view-specific　self-expressiveness　coefficient　matrices.　To　facilitate　a　practical　model,　we　parameterize　the　low-rank　structure　on　all　self-expressiveness　coefficient　matrices　through　the　tri-factorization　along　with　orthogonal　constraints.　This　specification　ensures　that　self-expressiveness　coefficient　matrices　of　different　views　have　the　same　rank　to　effectively　promote　structural　consistency　across　multiviews.　Such　a　model　can　learn　a　consistent　subspace　structure　and　fully　exploit　the　complementary　information　from　the　view-specific　self-expressiveness　coefficient　matrices,　simultaneously.　The　proposed　model　is　formulated　as　a　nonconvex　optimization　problem.　An　efficient　optimization　algorithm　with　guaranteed　convergence　under　mild　conditions　is　proposed.　Extensive　experiments　on　several　benchmark　databases　demonstrate　the　advantage　of　the　proposed　model　over　the　state-of-the-art　multiview　clustering　approaches.