The work aims to explore the potential of using near-infrared spectroscopy to classify common packaging materials. First, a NIR spectrometer was used to collect spectral data from 11 commonly used packaging materials, including PBS, HDPE, LDPE, LLDPE, PP, PS, POE, PPC, PBAT, PLA, and a blend of PGA+PBAT. The spectral data was then preprocessed using three techniques:Moving Average (MA), Standard Normal Variate (SNV) transformation, and Multiplicative Scatter Correction (MSC). Four pattern recognition methods, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, and Random Forest were utilized to develop qualitative discrimination models. Finally, the prediction performance of the models was compared based on evaluation metrics and confusion matrices. The combination of SNV preprocessing with the KNN algorithm yielded the best classification performance, achieving an accuracy of 97.03%. It is concluded that NIR spectroscopy provides a convenient, fast, and non-destructive method for plastic identification, which is advantageous for plastic recycling and reuse. The results indicate good application prospects for this method in sustainable material management.