“Mass spectrometry imaging (MSI) has significant value in the field of biological tissue detection, but the problem of signal loss is prominent at high spatial resolution. This study proposes a missing value imputation method based on artificial intelligence, which achieves high-quality reconstruction of missing signals through data-driven learning of complex distribution features of MSI data. The experimental results show that this method performs excellently on mouse kidney MALDI-MSI and human colon cancer DESI-MSI data, outperforming traditional methods, and has good cross platform adaptability. It provides an efficient and low-cost solution for improving MSI detection sensitivity and is of great significance for promoting biological analysis.”