Aircraft detection via deep learning is a popular field in remote sensing image analysis. However, given the limited perspectives of satellite imagery and high similarities in image appearance, aircraft type recognition remains a challenging task. The existing deep learning methods cannot be satisfactorily applied to fine-grained aircraft type recognition tasks, which require refined labels for datasets. With the aim of effectively recognizing aircraft types in remote sensing images, we propose an integrated target segmentation and key point detection method for aircraft type recognition.The proposed method combines an organic multitask deep neural network with a conditional random field and template matching algorithm to achieve high-precision recognition of aircraft types by pretraining, fine-tuning, and postprocessing. First, we performed target aircraft position and mask and keypoint recognition by deploying multitask learning and transfer learning technology. Second, to facilitate high-precision template matching in the later stage, we utilized an aircraft target mask refinement algorithm and a keypoint-based mask attitude adjustment algorithm to achieve boundary refinement of the recognition target and aircraft target mask attitude adjustment. Finally, on the basis of the aircraft type template library constructed in this study, we matched the refined aircraft mask information with the template library to identify the aircraft type.The proposed algorithm was applied to the MTARSI dataset and remote sensing images for verification. The results showed that the recognition accuracy of the 11 types of images was 89%. Aircraft with simple structures and unique shapes, such as B-2 and B-1, exhibited high recognition accuracy, whereas aircraft with complex structures and high similarity with other shapes, such as E-3 reconnaissance aircraft, exhibited low recognition accuracy. Subsequently, the algorithm was compared with traditional algorithms and end-to-end deep learning methods. Eleven types of aircraft were studied. The results showed that the accuracy of our method was 15.4% and 20.7% better than those of the other two methods.The use of target segmentation and keypoint information has achieved good results in model recognition on high-resolution remote sensing images. However, limitations remain in terms of the breadth of identifiable aircraft types; therefore, further research is needed to address this research gap.