The microseismic signals generated during the deformation and rupture of loaded composite coal rock contain information about the rupture of the internal structure of the coal rock, and the microseismic signals collected by traditional equipment cannot be analyzed directly because of the presence of a large amount of environmental noise. In order to effectively extract the change characteristics of the microseismic signals during the deformation and rupture of loaded coal rock, a new CEEMD-IDWT joint denoising algorithm is proposed by integrating the complementary ensemble empirical modal decomposition algorithm (CEEMD) with the improved dmey wavelet (IDWT) algorithm. The algorithm firstly utilizes the CEEMD algorithm to decompose the original signal, then applies the IDWT algorithm to denoise the IMF components obtained from the decomposition, and finally reconstructs the processed components to obtain the denoised signal. The effectiveness of the algorithm is verified using simulation analysis and uniaxial compression experiments, and the results show that: the CEEMD-IDWT joint algorithm improves the signal-to-noise ratio by a maximum of 204.5% compared with the traditional algorithm in simulation analysis, and increases the signal-to-noise ratio of other improved denoising algorithms by a minimum of 11.8%, which is an obvious advantage in denoising ability; the microseismic voltage obtained by embedding the algorithm into the self-researched microseismic voltage acquisition equipment is significantly higher than that obtained by the conventional algorithm in the uniaxial compression experiments on the composite coal rock. The noise-to-noise ratio of the microseismic voltage signal obtained in the compression experiment is only 0.089 75, and the actual denoising effect is obvious; the microseismic voltage after denoising by the joint CEEMD-IDWT algorithm has obvious change characteristics, which significantly improves the signal denoising effect and effectively avoids the distortion of the microseismic voltage signal, and can be used as an ideal algorithm for the denoising of deformation and rupture of the microseismic voltage signal of the loaded coal rock and provides an ideal algorithm to accurately predict the coal rock dynamics and disaster. It can be used as an ideal algorithm for de-noising the microseismic voltage signal of loaded coal rock deformation and rupture, which provides a reliable and advanced technical reference for the accurate prediction of coal-rock power disasters.