Abstract: An identification method based on fingerprint spectroscopy combined with decision tree, k-nearest neighbor, and Fisher discriminant analysis (DT-KNN-FDA) model was proposed to achieve the rapid and non-destructive identification of the vehicle paints and performed by theoretical analysis and experimental verification. The infrared absorption spectroscopy for a total of 60 samples of car paint were collected and obtained as the experimental data. Through the selection of characteristic wave numbers, a multi-classification model based on the DT, KNN analysis, and FDA was established and compared. 58 sets of adjustment data were extracted through correlation analysis, and a classification model was constructed based on this. The results show that the overall discrimination accuracy of DT classification model, KNN classification model and FDA classification model for each sample is 77.80%, 72.31%, and 85.00%, respectively; infrared spectroscopy combined with DT-KNN-FDA analysis can realize the distinction between products of different brands is ideal for classification. This method is fast, accurate, and effective, and has certain universality and significance.