Graph　representation　learning　is　the　encoding　of　graph　nodes　into　a　low-dimensional　representation　space,　which　can　effectively　improve　graph　information　representation　while　reducing　the　information　dimensionality.　To　overcome　the　heavy　reliance　on　label　information　of　previous　graph　representation　learning,　the　graph　contrastive　learning　method　has　received　attention　from　researchers　as　a　self-supervised　learning　method,　but　it　introduces　the　problem　of　sample　selection　dependence.　To　address　this　issue,　inspired　by　deep　clustering　methods　and　image　contrastive　learning　methods,　we　propose　a　novel　Siamese　network　method,　namely　Community-enhanced　Contrastive　Siamese　networks　for　Graph　Representation　Learning　(MEDC).　Specifically,　we　employ　a　Siamese　network　architecture　to　contrast　two　augmented　views　of　the　original　graph　and　guide　the　network　training　by　minimizing　the　similarity　of　positive　sample　nodes　and　negative　sample　nodes.　Meanwhile,　to　take　full　advantage　of　the　potential　community　structure　of　graph,　we　add　a　deep　clustering　layer　in　the　network　architecture,　and　the　perceived　community　structure　information　is　used　to　guide　the　selection　of　positive　and　negative　samples.　To　demonstrate　the　effectiveness　of　the　proposed　method,　we　conducted　a　series　of　comparative　experiments　on　three　real　datasets　to　validate　the　performance　of　our　method.