Semi-supervised　node　classification　with　Graph　Convolutional　Network　(GCN)　is　an　attractive　topic　in　social　media　analysis　and　applications.　Recent　studies　show　that　GCN-based　classification　methods　can　facilitate　the　accuracy　increase　of　learning　algorithms.　However,　most　of　the　existing　methods　do　not　conduct　adequate　explorations　of　the　complementary　information　within　the　topology　structure.　Besides,　they　also　suffer　from　the　insufficient　excavation　of　useful　information　among　nodes　and　the　scarcity　of　labeled　samples,　resulting　in　undesired　classification　performance.　To　cope　with　these　issues,　this　paper　proposes　a　contrastive　GCN-based　framework　to　jointly　leverage　the　topology　graph　and　the　self-adaptive　topology　graph　with　feature　information　in　semi-supervised　information.　In　order　to　extract　more　valid　potential　information　in　the　topology　graph　and　increase　the　flexibility　of　the　framework,　we　learn　an　adjacency　matrix　supervised　by　a　flexible　loss　that　exploits　node　embeddings　to　reinforce　the　topological　representation　capability　of　the　adjacency　matrix.　To　maximize　the　homogeneity　of　these　two　distinct　graphs,　we　design　an　improved　semi-supervised　contrastive　loss.　In　order　to　enrich　scarce　label　information,　we　propose　a　self-supervised　mechanism　to　generate　reliable　pseudo　labels　from　abundant　unlabeled　data,　which　further　refines　the　learnable　adjacency　matrix.　With　these　modules,　both　unlabeled　and　labeled　samples　jointly　furnish　the　supervision　signals,　thereby　improving　the　accuracy　of　the　proposed　model.　Extensive　experimental　results　on　real-world　datasets　demonstrate　the　effectiveness　and　superiority　of　the　proposed　algorithm　against　state-of-the-arts.