Aimed at the problem that conventional moving target detection methods for airborne radar always need many training range samples, there is an adaptation, i.e. transforming the target detection problem into a multiclassification problem. Firstly, the training dataset is constructed based on a small amount of training range samples, and then, a multiclass classifier is constructed based on DenseNet. Finally, the trained classifier is utilized for extracting the characteristics of the received spacetime data for target detection and parameter estimation. The simulation results show that the DenseNetbased airborne radar moving target detection method proposed can detect the target effectively, and estimate its distance, Doppler frequency, and other parameters. Compared with the conventional space time adaptive processing method, the proposed method can significantly reduce the number of needed training range samples. Compared with the existing target detection method based on classification, the proposed method can improve the accuracy of target detection and parameter estimation effectively.