Deck paints are important evidence materials in maritime criminal cases. The rapid, accurate, and nondestructive identification of deck paints is crucial in forensic science. Infrared spectra are commonly used for such a fast and nondestructive identification analysis. Pretreatments can eliminate the noise and background interference of the spectra and improve the recognition rate of the dataset. In an experiment, 100 samples from five brands of deck paints were collected. Support vector machine (SVM) and Bayes discriminant analysis (BDA) were used to identify the deck paint spectra after several infrared pretreatment combinations, and the optimal infrared pretreatment method was screened according to the recognition rate. Results show that the effect of BDA is better than that of SVM. The recognition rate of the training dataset of the deck paint spectra processed using Savitzky-Golay second derivative smoothing and Fourier self-deconvolution is 100%, and the recognition rate of the test dataset is 96%. The selected method shows a good classification effect and strong qualitative ability and can be used as a reference for identifying deck paints in forensic science.