Existing multi-station fusion technologies focus on utilizing intuitive features such as echo amplitude correlation and spatial location. However， the comprehensiveness of manual feature extraction is insufficient， which can easily lead to signal resource waste， incomplete feature extraction， and limited generalization of discrimination algorithms. To address this issue， this paper innovatively proposes a jamming identification strategy that integrates multi-radar cooperative detection with convolutional neural network. This approach leverages convolutional neural networks to deeply explore unknown information in echo data， extracting differences between real and false targets in multidimensional deep features， surpassing single spatial correlation differences， and achieving deception jamming identification. Finally， simulation experiments validate the feasibility of the proposed method in resisting deception jamming and analyze the effects of target size， multi-station radar deployment and phase errors on the proposed algorithm.