The　core　of　multi-view　clustering　is　how　to　exploit　the　shared　and　specific　information　of　multi-view　data　properly.　The　data　missing　and　incompleteness　bring　great　challenges　to　multi-view　clustering.　In　this　paper,　we　propose　an　innovative　multi-view　subspace　clustering　method　with　incomplete　graph　information,　so-called　incomplete　multiple　graphs　clustering.　Specifically,　we　creatively　separate　one　shared　and　multiple　specific　graphs　from　multiple　raw　graph　data,　and　exploit　the　mask　fusion　strategy　and　block　diagonal　regulariser　to　obtain　the　inherent　category　information.　To　handle　the　incomplete　multiple　graph　data,　we　utilise　multiple　indicator　matrices　to　mark　the　missing　elements　existed　in　each　raw　graph.　In　addition,　the　weight　of　each　raw　graph　is　adaptively　learnt　according　to　the　graph　importance.　The　alternative　direction　optimization　algorithm　is　employed　to　solve　our　proposed　methods.　Finally,　we　also　analyse　the　algorithm　convergence　and　the　computation　complexity　in　detail.　The　clustering　results　on　six　real-world　datasets　show　that　our　method　obviously　outperforms　a　serious　of　classic　incomplete　multi-view　clustering　methods.