Abstract: When using electromagnetic detection method to detect the damage of mine wire rope, the detection signal contains a lot of noise. Moreover, there are spikes and mutations interference, which increase the difficulty of damage identification. Therefore, it is necessary to reduce the noise of the original detection signal. The commonly used Fourier transform cannot process the operating wire rope detection signal. The wavelet transform has problems of poor translation invariance and frequency aliasing, which affect the detection accuracy. This paper proposes a method for mine wire rope damage detection based on dual-tree complex wavelet transform. Firstly, a dual-tree complex wavelet high and low pass filter is constructed by Q-shift method, and the original signal is decomposed by 3-layer dual-tree complex wavelet to obtain the high and low frequency signal components. Secondly, a soft threshold method with minimax variance is used to reduce the noise of the decomposed signal. Finally, the noise reduction signal is reconstructed. A wire rope damage detection test platform is built in the laboratory environment to verify the noise reduction performance of the wire rope damage detection signal processing method based on dual-tree complex wavelet transform. The results show that the method can reduce the number of spikes and mutations in the detection signal effectively and make the signal stable. The noise reduction effect is better than that of classical wavelet transform. The method increases the signal peak value at the singularity point, which is beneficial to the subsequent feature extraction.