Recently,　heterogeneous　graph　contrastive　learning,　which　can　mine　supervision　signals　from　the　data,　has　attracted　widespread　attention.　However,　most　existing　methods　employ　random　data　augmentation　strategies　to　construct　contrastive　views,　which　may　destroy　the　semantic　information　in　heterogeneous　graphs.　Moreover,　they　often　select　positive　and　negative　samples　based　solely　on　node-level　proximity　and　overlook　hard　samples　that　are　difficult　to　distinguish　from　anchors.　To　solve　the　above　problems,　we　propose　a　Community-Aware　Heterogeneous　Graph　Contrastive　Learning　model　called　CAHGCL.　In　particular,　we　design　an　adaptive　data　augmentation　strategy　to　construct　views,　including　feature　augmentation　and　topology　augmentation.　To　improve　the　quality　of　samples,　we　propose　a　dynamic　sample　weighting　strategy　based　on　node　similarity　and　community　information,　capable　of　identifying　both　hard　positive　samples　and　hard　negative　samples.　Finally,　we　introduce　community-level　contrast　to　improve　community　cohesion.　Extensive　experiments　and　analyses　demonstrate　that　CAHGCL　consistently　outperforms　state-of-the-art　baselines　on　three　datasets.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.