Multi　-view　clustering　has　attracted　widespread　attention　because　it　can　improve　clustering　performance　by　integrating　information　from　various　views　of　samples.　However,　many　existing　methods　either　neglect　graph　information　entirely　or　only　partially　incorporate　it,　leading　to　information　loss　and　non　-comprehensive　representation.　Besides,　they　usually　make　use　of　graph　information　by　determining　a　fixed　number　of　neighbors　based　on　prior　knowledge,　which　limits　the　exploration　of　graph　information　contained　in　data.　To　address　these　issues,　we　propose　a　novel　method,　termed　Graph　-Driven　deep　Multi　-View　Clustering　with　self　-paced　learning　(GDMVC),　which　integrates　both　feature　information　and　graph　information　to　better　explore　information　within　the　data.　Additionally,　based　on　the　idea　of　self　-paced　learning,　this　method　gradually　increases　the　number　of　neighbors　and　updates　the　similarity　matrix,　progressively　providing　more　graph　information　to　guide　representation　learning.　By　this　way,　we　avoid　issues　associated　with　a　fixed　number　of　neighbors　and　ensure　a　thorough　exploration　of　graph　information　contained　in　the　original　data.　Furthermore,　this　method　not　only　ensures　the　consistency　among　views　but　also　leverages　graph　information　to　further　enhance　the　unified　representation,　aiming　to　obtain　more　separable　cluster　structures.　Extensive　experiments　on　real　datasets　demonstrate　its　effectiveness　for　multi　-view　clustering.　Our　code　will　be　released　on　https://github.com/yff-java/GDMVC/.