Aiming at the problem that the internal defect detection signal of gear is easily disturbed by noise and it is difficult to extract the defect feature information, an ultrasonic detection signal of gear defects denoising method based on dynamic local entropy and adaptive decomposition is proposed. The empirical mode decomposition is used to decompose the ultrasonic detection signal of gear defects adaptively, and the preprocessed signal is obtained. The local entropy distribution of preprocessed signal is calculated by dynamic local entropy theory, the defect echo interval is determined with the local entropy threshold, and the defect echo signal is then obtained. The defect echo signal is denoised based on the empirical wavelet transform, and the signal smoothness is improved through the quadratic polynomial least square algorithm. The final denoised result of the ultrasonic detection signal of gear defects is obtained. Simulations and actual measurement experiments are carried out to verify the denoising performance of the proposed method. The simulation experiment results show that for simulation signals under different SNR conditions, the mean SNR of the proposed method after denoising is 21.34 dB, and the mean square error MSE is 0.000 2 V. The denoising effect of the proposed method is significantly better than EMD and Wavelet Transform. The mean SNR and mean square error of the two methods are 10.43 dB, 12.56 dB and 0.001 9 V and 0.001 4 V respectively. In addition, the proposed method has better denoising effect and stronger robustness under different SNR conditions. The experimental results show that the proposed method can remove the complex noise interference in the ultrasonic detection signal of metal gear defects, and effectively improve the quality of the ultrasonic detection signal of metal gear defects.