Plastics are widely used in daily life and industry because of their plasticity and low costs. However, they cause problems, such as environmental pollution and resource waste, and plastic classification has become an important research topic. Near-infrared hyperspectral imaging (NIR-HSI) is used to compare the effect of 1100‒1650 nm band data in classifying nine common plastics to verify the feasibility of hyperspectral imaging in plastic sorting. Machine learning methods such as the K-neighborhood method (K-NN), support vector machine (SVM), SVM trained by particle swarm algorithm (PSO-SVM), and SVM optimized by genetic algorithm (GA-SVM) are used. After verifying the accuracy of the data screening model, it is applied to hyperspectral images, and the model effect is evaluated by comparing the original images through visual classification. The results show that the K-NN and GA-SVM based on the Euclidean distance and cosine similarity are the most effective in classification, and the accuracy of the validation data reaches 96.14%, 96.21%, and 98.67%, respectively. Good results are also presented in the visualization classification. The experiment demonstrates that hyperspectral imaging technology has high application value in plastic sorting. This can effectively differentiate similar plastic products based on color, shape, and process by acquiring the spectral data of specific plastics and processing them appropriately.