The work aims to realize the rapid detection and material differentiation of food plastic packaging bags. The spectral data of 49 groups of different food packaging bags were collected by hyperspectral imaging technology in the wavelength range of 450～950 nm. The data were preprocessed by savitzky Golay smooth filtering, data normalization and principal component analysis to establish two traditional machine learning models of decision tree and SVM and one convolutional neural network model. Then, the recognition performance of traditional machine learning models and convolutional neural network model on the packaging bag materials was compared. The verification recognition rate of decision tree model and SVM model was 87.8% and 88.9%, respectively, while the verification recognition rate of convolutional neural network model was up to 100%, and the loss function value finally dropped to 0.0171 and tended to be stable. Therefore, the convolutional neural network model had obvious advantages in classification effect and accuracy. The method of hyperspectral detection does not destroy the material, and has good reproducibility and strong stability, which can realize the accurate identification of food plastic packaging bags. The convolutional neural network model has the best recognition effect on hyperspectral data of food packaging bags and provides the basis for the identification and recognition of plastic packaging bags in the field of food packaging quality detection.