Existing depth-learning target detection algorithms are unsuitable for air-to-ground target detection because the results are degraded by the single imaging angle, target size changing with imaging height, and complexity of the background interference. To solve this problem, this paper proposes a attention learning mechanism in key areas, which enhances the expressive ability of the feature maps and alleviates the interference of complex background features. This paper first establishes the proposed learning mechanism, which enables the network to select and utilize the features of the target regions in images. Second, it designs a loss function coupled with regional attention and target detection for synchronous optimization of the regional attention loss and target detection loss, which is then achieved by data mining. The proposed algorithm is experimentally evaluated on air-to-ground target detection datasets. The algorithm effectively focuses on and utilizes the feature information of the target key areas, reduces the interference of the background information, and improves the accuracy and anti-interference ability of air-to-ground target detection.