Recently,　graph　contrastive　learning　(GCL)　has　received　considerable　interest　in　graph　representation　learning　for　its　robustness　in　capturing　complex　relationships　between　nodes　in　an　unsupervised　manner,　making　it　suitable　for　unsupervised　graph　learning　tasks　such　as　community　detection.　However,　most　GCL　approaches　have　two　limitations　when　applied　to　community　detection.　First,　the　random　augmentation　strategy　employed　by　them　may　destroy　a　graph＇s　community　structure　due　to　the　random　added/removed　edges　or　attributes.　Second,　nodes　with　similar　topology　or　attributes　may　be　selected　as　the　negative　samples　of　a　target　node　according　to　their　sample　selection　strategy,　leading　to　the　wrong　assignment　of　the　target　node＇s　community.　In　this　paper,　we　propose　an　adaptive-graph-contrastive-learning-based　community　detection　(AGCLCD)　algorithm　to　address　the　problems.　At　its　core,　AGCLCD　introduces　an　adaptive　graph　augmentation　strategy　to　preserve　a　graph＇s　original　community　structure　in　augmentation.　Furthermore,　we　develop　a　composite　contrastive　pair　selection　scheme　to　choose　the　nodes　sharing　similar　topology　and　attributes　with　a　target　node　as　its　positive　samples　to　ensure　that　the　representation　vectors　of　nodes　in　the　same　community　are　highly　relevant.　Comprehensive　experiments　on　real-world　and　synthetic　networks　demonstrate　that　AGCLCD　achieves　higher　accuracy　and　effectiveness　than　state-of-the-art　algorithms.