Abstract: The wire rope broken wire damage detection is mainly focused on the outside broken wire damage detection, not on the inside broken wire damage detection. Moreover, the inside and outside broken wire identification accuracy is not high. In order to solve the above problems, this paper proposes a wire rope inside and outside broken wire damage identification method. The magnetic flux leakage signal generated by the wire rope broken wire damage is collected by the wire rope damage radial magnetic flux leakage detector. The double-density dual-tree complex wavelet transform is used to reduce the noise of the magnetic flux leakage signal. By setting an adaptive threshold, it is able to extract the time domain characteristics of the noise reduction signal and extract the frequency domain characteristics of the original magnetic flux leakage signal at the same time. A method based on the distance between classes and mutual information is used for characteristics selection. Firstly, all characteristics are normalized to eliminate characteristics with large standard deviation and small distance between classes. Secondly, the mutual information between characteristics is calculated to exclude characteristics with similar damage information. Thirdly, the two damage types with the worst discrimination among the characteristics are calculated, and the characteristics with the largest distance between these two types are retrieved from the eliminated characteristics. The retained characteristics are fused as the optimal characteristic subset and input into the BP neural network for classification and recognition. The test results show that the method can identify broken wire damage inside and outside the wire rope with a recognition accuracy of 97.8%.