With　the　expansion　of　graph　data　in　the　real　world,　unsupervised　graph　representation　learning　shows　greater　potential.　Unsupervised　graph　representation　learning　is　mainly　about　extracting　high-level　representation　from　the　rich　properties　and　structures　of　graphs.　In　this　paper,　we　propose　Graph　Representation　Contrastive　Learning　Framework　based　on　mutual　information(CgI).It　extracts　the　effective　topology　structural　information　and　context　of　the　graph　through　maximizing　the　node-level　and　graph-level　mutual　information　in　two　perspectives　respectively.　Node-level　mutual　information　mainly　focuses　on　local　association　information　between　nodes,　while　graph-level　mutual　information　is　more　concerned　with　the　guiding　role　of　global　information.　CGI　combines　contrastive　learning　and　mutual　information　into　feature　extraction.　The　experimental　results　confirm　that　the　proposed　model　performs　outstanding　improvements　contrast　with　the-state-ofthe-art　models,　and　it　has　better　representative　learning　ability.　©　2021　IEEE.