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In electromagnetic gun testing, the muzzle generates strong fire signals and noise such as vibration, which can seriously interfere with the recognition processing of armature feature signals. In order to improve the automatic recognition rate of armature signals, an armature signal recognition method based on the combination of wavelet transform and Convolutional Neural Network (CNN) is proposed. Firstly, wavelet transform is used to denoise the over-target signal with wavelet threshold, and then reconstruct the signal. Secondly, the deep features of the signal are extracted using CNN, and the classification results of the signal are output through the fully connected layer of CNN. Finally, when the input signal is an armature signal, the maximum detection is performed to obtain the feature points of the armature signal. The experimental results show that the proposed method improves 5.88% in feature point automatic picking accuracy compared with the traditional wavelet threshold filtering method. The algorithm has certain reference significance for the filtering and identification of electromagnetic gun armature over-target signals.