The identification of individual radiation sources is an important technology in the field of electronic countermeasures. By identifying different subtle features between devices, the purpose of distinguishing illegal devices from legal devices is achieved. Aiming at the problem of subtle differences in fingerprint features between individual radiation sources and fewer features extracted under noise interference, this paper proposes a method of identifying individual radiation sources based on a deep residual shrinkage network. This method first splices the feature data of the I/Q map, uses data enhancement technology to expand the sample, and then constructs a deep residual shrinkage network recognition model. Finally the constructed model is trained for individual ADS-B radiation source recognition and the recognition effect is evaluated. The simulation results show that the deep residual shrinkage network constructed in this paper uses the advantage of eliminating data noise, and the overall recognition accuracy of the 20 types of ADS-B radiation source individuals after data enhancement has reached 98.2% when the SNR is as low as 0 dB.Compared with the Resnet network with the same number of layers, its performance is improved by 1.3%, and it is significantly better than other existing methods.