Estimating　the　missing　part　is　an　important　branch　of　point　cloud　completion　research.　However,　the　existing　point　cloud　completion　methods　ignore　the　importance　of　feature　differences　between　partial　and　complete　point　clouds,　and　have　not　further　optimized　the　predicted　missing　point　cloud.　For　this　reason,　we　propose　an　effective　Graph　based　Missing　Part　Patching　Network　(GMP-Net),　which　is　a　deep　learning　model　that　uses　the　power　of　graph　convolution　to　predict　missing　part　of　point　cloud　in　a　patching　manner.　First,　GMP-Net　improves　its　feature　extraction　ability　through　double　perspective　contrastive　learning　which　extracts　better　global　features　with　semantic　boundaries　between　missing　and　incomplete　point　clouds.　Second,　a　tree　structure　decoder　combined　with　graph　attention　is　proposed　to　generate　intermediate　coarse　missing　points　by　gathering　topologies　from　ancestors＇　information.　Third,　we　propose　a　new　graph　convolution　technique　that　refines　intermediate　generation　by　constructing　a　graph　between　partial　and　missing　points.　This　approach　optimizes　the　generated　missing　points　with　the　local　feature　of　the　partial　points.　Extensive　experiments　demonstrate　the　effectiveness　of　the　proposed　GMP-Net.