Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has become a robust tool for analyzing a variety of biomacromolecules. However, the strong background interference produced by conventional organic matrices hinders the detection of small molecule analytes, which restricts the widespread application of MALDI-MS in metabolomics studies. Consequently, developing new organic matrices is urgently needed to overcome these issues. In this study, 1,4-Dioxo-1,2,3,4-tetrahydrophthalazine-6-carboxylic acid (DTCA) was firstly employed as a new matrix for MALDI-MS to enhance the detection of low molecular weight compounds because of its strong UV absorption, less matrix background interference, high ionization efficiency for metabolites, and good reproducibility. Considering these advantages, DTCA was used to analyze endogenous metabolites in the serum samples of imidacloprid (IMI)-exposed mice via MALDI-MS in positive ion mode. By combining with machine learning, the differentiation between imidacloprid-exposed mice and control mice was successfully achieved, and 39 metabolites were estimated as potential biomarkers. Additionally, potentially disrupted metabolic pathways were revealed. These results indicate that DTCA, as a new and powerful matrix in positive ion mode, has great potential for applications in the detection of small molecules.