In　the　coal　mining　production　process,　wire　ropes　play　a　crucial　role　as　the　main　tool　for　transporting　personnel　and　materials　in　the　entire　coal　mine　hoisting　system,　and　their　fault　identification　is　particularly　critical.　To　address　the　problem　of　fault　identification　of　mining　wire　ropes,　a　recognition　model　design　combining　K　Singular　Value　Decomposition　(SVD)　with　Genetic　Algorithm　(GA)　optimized　Support　Vector　Machine　(SVM)　is　proposed.　Firstly,　K　Singular　Value　Decomposition　is　used　to　denoise　the　collected　wire　rope　damage　signals.　Feature　vectors　are　extracted　from　the　denoised　and　reconstructed　signals　to　form　the　feature　set　of　wire　rope　damage.　Genetic　Algorithm　is　utilized　to　select　the　optimal　parameters　of　the　Support　Vector　Machine　and　train　it　to　obtain　the　optimal　identification　model　for　wire　rope　damage.　Simulation　and　experimental　results　show　that　compared　with　wavelet　thresholding　and　Empirical　Mode　Decomposition　(EMD)　denoising　algorithms,　the　method　using　K　Singular　Value　Decomposition　for　denoising　achieves　a　signal-to-noise　ratio　of　25.166dB　and　a　root　mean　square　error　of　0.805.　Compared　with　Particle　Swarm　Optimization　(PSO)　optimized　Support　Vector　Machine　identification　model,　the　identification　rate　of　the　Support　Vector　Machine　identification　model　optimized　by　Genetic　Algorithm　is　higher,　reaching　96.67%.　This　method　realizes　accurate　identification　of　different　damage　signals　of　wire　ropes,　which　is　conducive　to　ensuring　the　safe　and　efficient　production　of　coal　mines.　©　Published　under　licence　by　IOP　Publishing　Ltd.