To achieve fast, non-destructive, and accurate classification of paint evidence, the infrared raw and derivative spectral data of fifty paint samples from five common crime scenes were collected. The paint classification models based on KNN, SVM, and stepwise discriminant analysis were created using spectral fusion technology. The experimental results show that the three classification models for the fusion spectrum have a higher recognition rate than a single spectrum. The recognition rate of the KNN and SVM classification model for three paint samples is high, but the classification effect for the remaining two samples is not good. The recognition rate of the stepwise discriminant analysis completely model for all kinds of spectral data of five paint samples is higher than that of the KNN and SVM models. To achieve 100% recognition of the training and test sets, the Smallest F ratio discriminant model of stepwise discriminant analysis identifies the first derivative and third derivative spectral fusion data. This method has high efficiency and strong qualitative ability, and it meets the requirements for rapid inspection of relevant material evidence by public security organs. It also provides criminal technicians a quick way to identify paint evidence.