With the large-scale application of Digital Radio Frequency Memory (DRFM) in electronic countermeasures equipment, coherent forwarding jamming has gradually become the mainstream of jammer selection. This type of interference is perfectly suited to DRFM hardware, and can also make the interference waveform obtain the system gain of the radar side, achieving good interference effect. To realize the classification and identification of such interference, the Wavelet Neural Network (WNN) in the field of computer is introduced into the field of electronic countermeasures for the first time in this paper, and the network is improved to identify the type of coherent relaying jamming for Pulse Doppler (PD) radar. According to the simulation results, compared with the existing Long Short-Term Memory (LSTM) neural network, the proposed method can improve the recognition accuracy by about 30% under the same parameter scale.