In view of the large quantity and variety of waste plastic recycling, it is difficult to quickly and nondestructive identification, a plastic material identification method based on near infrared spectroscopy was proposed. The near-infrared spectral data of eight kinds of plastics, namely polyethylene terephthalate (PET), polyethylene (PE), nylon (PA), polycarbonate (PC), polypropylene (PP), polystyrene (PS), acrylonitrile-butadiene-styrene (ABS) and polyformaldehyde (POM), were collected by infrared spectrometer. Savitzky-Golay convolution smoothing and standard normal variable transformation were used for spectral data preprocessing. Unsupervised learning principal component analysis and supervised learning linear discriminant analysis (LDA) were used respectively to reduce the dimension of spectral data. The dimensions of the spectral data are reduced from 334 to 10 and 7. Finally, the identification model of plastic material is established based on Mahalanobis distance discrimination. The experimental results show that the combination of S-G smoothing and SNV preprocessing effectively improves the recognition accuracy. After dimensionality reduction of the validation set of the preprocessed data, the recognition accuracies of the two dimensionality reduction methods reached 95.24% and 100% respectively. These two methods can provide reference for the identification of various waste plastic materials.