Brain network analysis significantly improves artificial intelligence techniques in the realm of digital health. Most existing methods uniformly construct brain networks for different groups (e.g., male and female groups, healthy and sick people groups), facing the interference of group-irrelevant noises and failing to capture group-specific features to enhance brain networks. To address this issue, this paper proposes an adaptive brain network augmentation method based on group-aware graph learning. We construct group-aware brain networks, which can adapt to distinct groups, reducing the interference of noises, and improving model robustness across various tasks and subject groups.