Magnetic acoustic emission （MAE） is an acoustic emission signal generated in the magnetization process of ferromagnetic materials， which has a wide range of applications in component stress detection and micro damage detection. Aiming at the characteristics of MAE signal instability， complexity， and attenuation， a denoising method based on seagull optimization algorithm combined with variational mode decomposition （SOA-VMD） is proposed. In order to overcome the problem of getting into the local optimal solution in the solving process of the seagull algorithm， we use the Cauchy variation operator to generate random iterations， making Cauchy variation seagull optimization algorithm （CVSOA） to jump out of premature convergence. The amplitude spectrum entropy is used as the fitness function， and the SOA is used to optimize the number of decomposed modes K and secondary penalty term α in the VMD algorithm. Then， the noisy signal is decomposed by VMD， and the MAE signal is reconstructed after removing the noise component. The analysis of the simulated signal and the actual detection signal shows that the improved CVSOA-VMD algorithm’s global optimization ability and denoising performance are better than the traditional SOA-VMD algorithm， the noise reduced MAE signal eigenvalues have better repeatability and higher reliability for root mean square and skewness eigenvalues under different stresses.