Social　recommender　systems　based　on　graph　neural　networks　can　successfully　model　the　social　influence　among　users　and　effectively　mitigate　the　issue　of　data　sparsity.　However,　existing　methods　mostly　focus　on　individual　influence　while　ignoring　group　influence.　The　social　influence　exerted　by　different　groups　of　users　can　vary,　and　the　intensity　can　also　vary.　A　user　is　often　more　susceptible　to　the　influence　of　groups　with　preferences　similar　to　his/her　own.　This　paper　proposes　a　social　recommendation　model　called　Multi-channel　Graph　neural　network　with　Contrastive　Learning　(MGCL).　MGCL　classifies　users　into　different　groups　according　to　their　preferences,　establishes　separate　social　propagation　channels　for　each　group,　and　extracts　the　influence　of　different　user　groups　to　better　represent　a　user.　In　addition,　contrastive　learning　is　utilized　to　improve　the　modeling　of　user　preferences　and　social　influence　during　model　training.　The　superiority　of　the　proposed　MGCL　over　state-of-the-art　baselines　has　been　demonstrated　through　extensive　experiments　conducted　on　three　benchmark　datasets.