The work aims to establish a fast and non-destructive analysis method for inspecting plastic food packaging bottles and provide a fast classification model. 100 plastic food packaging bottle samples were tested by differential Raman spectroscopy. The samples were classified based on their differential Raman characteristic peaks and divided into two categories of polyethylene terephthalate and polypropylene. The Class I samples in a larger number were further classified based on the different fillers contained. The classification model was constructed by Bayesian discriminant analysis, multi-layer perceptron, and random forest algorithm to analyze and verify the continued classification results.The Class I samples were further divided into four categories. The classification accuracy of Bayesian discriminant combined with left one cross validation method was 71.7%, the classification accuracy of the training and testing sets of the multi-layer perceptron neural network classification model was100% and 86.2%, respectively, and the classification accuracy of the random forest classification model0020was 100% and 96.5%. Through comparison, it was found that the combination of differential Raman spectroscopy and random forest algorithm could effectively classify plastic food packaging bottles.This method is simple and fast, requiring a small sample size but not damaging samples, which can provide scientific basis for the identification of physical evidence in plastic food packaging products.