Large-scale　Multi-View　Clustering　(LMVC)　is　a　hot　research　problem　in　the　fields　of　signal　processing　and　machine　learning,　and　many　anchor-based　multi-view　subspace　clustering　algorithms　are　proposed　in　recent　years.　However,　most　existing　methods　usually　concentrate　on　the　issue　of　reducing　the　time　cost　and　ignore　the　exploration　of　the　complementary　information　during　the　clustering　process.　To　this　end,　we　propose　a　Fast　Essential　Subspace　Representation　Learning　(FESRL)　method　for　large-scale　multi-view　subspace　clustering.　Specifically,　FESRL　introduces　the　orthogonal　transformation　to　investigate　both　the　complementary　and　consensus　information　across　multiple　views.　The　essential　subspace　representation　can　be　learned　in　a　linear　time　cost.　Experiments　conducted　on　several　benchmark　datasets　illustrate　the　competitiveness　of　the　proposed　method.