A method for mine roller fault acoustic source localization based on function deconvolution optimization was proposed to address the problems of frequency aliasing and large localization error in mine belt conveyor systems. The cross-spectral matrix function and the point spread function in the traditional fault acoustic source imaging function were reconstructed and analyzed. By optimizing the acoustic source localization function, the resolution of fault signals was improved, thereby enhancing localization accuracy and ensuring accurate identification of mine roller fault positions. First, the peak cross-spectral matrix function caused by sidelobes in the initial localization acoustic source map was calculated. The corrected cross-spectral matrix function, obtained by removing the peak cross-spectral matrix, was used as the target cross-spectral matrix function for reconstruction and optimization. Then, the reconstructed cross-spectral matrix function was decomposed into eigenvalues, and its eigenvectors and eigenvalues were used to form a unitary matrix and a diagonal matrix. Finally, an element-wise power transformation was performed on the acoustic source localization function, and an element-wise root transformation was applied to the cross-spectral matrix function to obtain the reconstructed and analyzed point spread function. A function deconvolution-optimized equation set for mine roller fault acoustic source localization was then established. To improve the computational efficiency of the function deconvolution optimization, an accelerated greedy iterative update strategy was adopted to achieve fast convergence to the localization solution. To address the problem of frequency aliasing in fault acoustic sources, a variational mode decomposition optimized by an improved sparrow search algorithm and an improved multi-scale dual-threshold wavelet cooperative hierarchical adaptive denoising method were applied to enhance the extraction of fault feature frequencies. Experimental results show that, compared with common mine roller fault acoustic source localization methods, the proposed method effectively suppresses sidelobe interference in acoustic imaging maps and improves the localization accuracy of mine roller faults. The optimized algorithm achieves a localization accuracy of 94.42% and increases computational efficiency by approximately 3.8 times. The signal-to-noise ratio and peak signal-to-noise ratio of the cooperative hierarchical adaptive denoising are improved by 88.25% and 56.06%, respectively, indicating significant gains in both accuracy and efficiency.