In view of the potential impact of secondary path mutation on the convergence speed and system stability of the algorithm, an adaptive variable step size FXLMS algorithm is proposed. Firstly, the formula of the upper bound of the step size is derived, and the key parameters are established based on the relationship between the optimal convergence step size and the upper bound of the step size. By comparing the optimal step size ratio before and after the mutation of the secondary path, the adaptive adjustment of the step size was realized. Secondly, in order to compare the performance of the proposed algorithm, the classical FXLMS algorithm and various variable step size algorithms in terms of convergence speed and steadystate error, it is found that the new algorithm converges after 200 iterations, and the mean square error remains around -85 dB, which is better than the classical FXLMS and other variable step size algorithms. Finally, the control effect of the new algorithm and the classical FXLMS algorithm is analyzed by using the secondary path data of real measurements. The results show that after the mutation of the secondary path, the mean square error of the new algorithm is stable at -47 dB after 5 s, while the classical FXLMS algorithm will make the system unstable. It is proved that the new algorithm can take into account the convergence speed and steady-state error well, and has good adaptability.