Abstract: With the development of food industry and improvement of people’s living standards, people’s demand for high-value and high-nutrition food is also growing. Meat adulteration is becoming more and more serious driven by economically motivated adulteration (EMA). In this situation, detection technology of meat authenticity has become a research hotspot in the field of food science. Rapid detection technology is of great significance for traceability and real-time detection of food supply chain. In this study, the key performance indicators for rapid identification of meat and aquatic food species by rapid evaporation ionization mass spectrometry (REIMS) were studied. An intelligent knife was used to cut the sample tissue to release aerosols, which were then directly inhaled into the mass spectrometry through a catheter for analysis and detection. Phosphatidylinositol is stable in meat, which was used as the internal standard. The data were normalized with total ion chromatography. The peaks with signal-to-noise ratio greater than 10 were selected by Live ID software, and the model was constructed by principal component analysis combined with linear discriminant analysis. At the same time, the sample data were divided into three groups: livestock meat, poultry meat and aquatic products. Then, the model was established and simulated for identification and evaluation. After that, according to biological classification or sensory similarity, each group of samples was modeled and simulated. Resolution, discrimination rate and stability of this method were determined for animal species identification, through parameter optimization, data acquisition and model establishment of 12 species of livestock, 6 species of poultry and 13 species of aquatic products. The results indicated that the recognition accuracies of discriminative models of livestock, poultry and aquatic products were 96.56%, 99.71% and 96.33%, respectively. Then, Progenesis QI software was used to analyze the difference of mass spectrometry information between livestock and poultry samples, then LIPID MAPS lipid structure inference tool was used to infer the structure. 14 species of livestock meat characteristic components and 12 species of poultry meat characteristic components were identified by differential factor analysis. The main difference factors between livestock and poultry were phospholipids, such as phosphatidic acid, phosphatidylcholine, phosphatidylserine, phosphatidylglycerol and sphingomyelin. Poultry and livestock meat were selected for analysis, the results showed that REIMS technology could analyze the difference factors while making rapid discrimination. REIMS technology can accurately identify the different factors of different samples, thus laying a foundation for subsequent methods verification, comparison, standardization and quantitative research, which has good application prospects in the field of food authenticity and quality rapid detection.