<p>Spatially resolved metabolomics based on mass spectrometry imaging (MSI) enables in situ characterization of tissue-specific metabolic functions by mapping the spatial distribution of metabolites. However, accurate metabolite annotation and automated analysis of large-scale MSI data remain challenging, mainly due to mass-to-charge ratio (<i>m/z</i>) shifts and dependence on generic databases. To address these challenges, we developed the MSI Data Analysis Tool (MSIDAT), an automated and user-friendly MSI data processing platform. By integrating liquid chromatography-tandem mass spectrometry (LC-MS/MS)-assisted metabolite identification, customized metabolite ion databases can be constructed to improve the specificity and reliability of metabolite annotation. In addition, <i>m/z</i> shifts in MSI data were systematically evaluated using endogenous reference ions by calculating the relative mass error between theoretical and measured <i>m/z</i> values, enabling adaptive mass tolerance correction. Based on this strategy, mass error-informed metabolite matching and putative annotation were achieved. Furthermore, MSIDAT provides flexible parameter settings, modular workflows, and open-source accessibility, facilitating efficient and reproducible MSI data analysis. The performance of the platform was demonstrated in a clinical cohort of rectal cancer patients, in which hundreds of metabolites were putatively annotated and spatial alterations in tumor-associated metabolites were observed, suggesting fatty acid-related metabolic alterations. Overall, this study presents a robust and versatile analytical platform for improving metabolite annotation in MSI, thereby enhancing data mining efficiency and supporting spatial metabolomics-driven biomarker discovery and clinical applications.</p>