Noise feedback active control systems usually have the disadvantage of slow convergence and high control residuals. This paper has designed a Nearest Neighbors-Multifrequency Notch-Filter (NNR-MNF) model for dealing with line spectral noise. It calculates the approximate solution of the optimal filter coefficients in the time domain and then iterates the filter parameters from a position close to the optimal solution. This procedure allows the system to control complex noise by using a small learning rate. As a result, it can reach rapid convergence while avoiding the problem of system divergence. The experiment results based on the destroyer engine noise dataset show that the NNR-MNF algorithm reduces the convergence time by about 70% compared with the traditional control method. The results show that the use of neural network methods based on machine learning can effectively improve the control speed of noise active control systems, and provide a new optimization solution for active noise control problems.