The airport area has great significance to both civilian and military use because of its particularity. At the same time, the airport area detection method based on machine self-identification is the current mainstream detection method. Aiming at the problem of insufficient robustness of traditional detection algorithms to the detection of multiple categories, multiple scales, multiple perspectives, and complex backgrounds in airport remote sensing images,an improved regional convolutional neural network detection algorithm is proposed. Firstly, compared with the traditional data set, a typical target data set of 7 types of airport areas under more conditions such as scales, perspectives, categories, and complex backgrounds is constructed and optimized, which lays a foundation for the supervised training and adjustment of model algorithms. Then, according to the characteristics of the detected target and the limitations of the network, the difference value method is used to generate the anchor, the complex negative sample screening, and the prior decision network are added to optimize and simulate the original network. Finally, the optimized network model is tested and compared. Experimental results show that the proposed algorithm has higher average accuracy than the original algorithm on the basis of increasing only a small amount of detection time, and achieves better results for various types of targets.