Multi-view　learning　is　a　promising　research　field　that　aims　to　enhance　learning　performance　by　integrating　information　from　diverse　data　perspectives.　Due　to　the　increasing　interest　in　graph　neural　networks,　researchers　have　gradually　incorporated　various　graph　models　into　multi-view　learning.　Despite　significant　progress,　current　methods　face　challenges　in　extracting　information　from　multiple　graphs　while　simultaneously　accommodating　specific　downstream　tasks.　Additionally,　the　lack　of　a　subsequent　refinement　process　for　the　learned　graph　leads　to　the　incorporation　of　noise.　To　address　the　aforementioned　issues,　we　propose　a　method　named　generative　essential　graph　convolutional　network　for　multi-view　semi-supervised　classification.　Our　approach　integrates　the　extraction　of　multi-graph　consistency　and　complementarity,　graph　refinement,　and　classification　tasks　within　a　comprehensive　optimization　framework.　This　is　accomplished　by　extracting　a　consistent　graph　from　the　shared　representation,　taking　into　account　the　complementarity　of　the　original　topologies.　The　learned　graph　is　then　optimized　through　downstream-specific　tasks.　Finally,　we　employ　a　graph　convolutional　network　with　a　learnable　threshold　shrinkage　function　to　acquire　the　graph　embedding.　Experimental　results　on　benchmark　datasets　demonstrate　the　effectiveness　of　our　approach.