Abstract: To further improve the success rate of detecting and identifying airborne targets under few shot conditions, a few shot target detection method based on background suppression and classification correction was proposed. Firstly, aiming at the problem that the background foreground of incoming air targets was easy to confuse, a background suppression module was introduced in the front end of the regional candidate network, which enhanced the foreground features by suppressing the background features and reduced the influence of the target background on detection. Secondly, the feature aggregation module was inserted after the background suppression module to focus on the target features, and to alleviate the problem that the target features were difficult to extract and not obvious due to few shot conditions, so as to correct the classification parameters of the network model. Finally, a contrast branch was introduced into the detection head network for enhancing the similarity within classes and uniqueness between classes, which alleviated the problem of high similarity between classes and large differences within classes of incoming targets, and realized the further correction of the network classification. The experimental results show that the proposed algorithm performs best in the 1, 2, 3, 5 and 10 shot experiments, with average accuracy reaching 28.3%, 32.8%, 39.9%, 42.9% and 56.2%, respectively, which improves the detection performance of few shots airborne incoming targets.