Abstract: We propose a noise reduction method for the acoustic emission of coal and rock based on wavelet transform and compressed sensing theory because the acoustic emission signals of coal and rock mass are random, non-stationary, and susceptible to noise signals. The proposed method utilizes the noise signal dispersion of the wavelet decomposition of the compression based on the perception of compression refactoring and noise reduction processing to ensure that the noise signal will be maximally separated from the coal and rock acoustic emission signal. The proposed method not only achieves noise reduction but also avoids threshold selection in wavelet filtering effect with respect tothe noise reduction effect. Further, different compression ratios, observation matrix, and reconstruction algorithm are used to compare the noise reduction effects of the wavelet threshold filtering, wavelet packet noise reduction method, and compression sensing method. The simulation results denotethat the proposed method can effectively suppress the interference of noise signals, improve the signal-to-noise ratio, and reduce the mean square error.