For　a　multi-view　learning　task,　it　is　crucial　to　assign　appropriate　weights　to　each　view　in　order　to　learn　complementary　and　consistent　information　across　different　views.　In　the　field　of　multi-view　clustering,　most　existing　methods　have　been　able　to　handle　the　weights　of　different　views.　However,　these　algorithms　face　the　problem　of　unacceptable　time　complexity　when　dealing　with　large-scale　datasets,　and　the　learned　similarity　matrix　fails　to　satisfy　the　graph　regularization.　In　this　paper,　we　propose　an　auto-weight　learning　method　called　multi-view　clustering　with　graph　regularized　optimal　transport.　First,　an　anchor-based　method　is　employed　to　overcome　the　problem　of　heavy　time　complexity　when　processing　large-scale　datasets,　and　it　is　able　to　automatically　learn　an　appropriate　weight　for　each　view.　Second,　by　introducing　optimal　transport　we　learn　a　regularized　doubly-stochastic　similarity　matrix　applicable　to　multi-view　clustering　tasks.　Third,　the　optimal　regularized　anchor　graph　can　be　classified　into　specific　clusters　by　adding　a　rank　constraint.　Finally,　an　effective　optimization　method　is　designed　to　optimize　the　formulated　problem.　Comprehensive　experiments　on　multiple　real-world　datasets　demonstrate　that　the　proposed　algorithm　achieves　superior　performance　to　other　state-of-the-arts　algorithms.　©　2022　Elsevier　Inc.