A series of inspection, analysis and data processing methods were proposed for common pharmaceutical aluminum-plastic packaging blister at the scene of cases in order to achieve the purpose of classification and identification. X-ray fluorescence spectrometry was used to test and analyze the elements contained in 45 pharmaceutical aluminum-plastic packaging blister samples. Unsupervised systematic clustering was performed on the test results, and the Euclidean distance was calculated by using the sum of squared deviation method to classify the unknown samples into 5 categories. The classification results were observed as discriminant analysis variables. Two discriminant functions with a cumulative variance percentage of 97.8% were selected and the Wilks' lambda is 0.015 and 0.394, which has the strongest explanatory ability. Finally, the five types of samples were distinguished from each other, and the overall discrimination accuracy rate was 95.6%. In order to achieve the purpose of pattern recognition of samples of unknown categories, extract the discriminant score of the samples on the discriminant function to construct an artificial neural network. The final classification accuracy rate was 97.8%. X-ray fluorescence spectroscopy was used to test the pharmaceutical aluminum-plastic packaging blister, and the types and contents of elements were classified as variables and an artificial neural network classification model of 45 pharmaceutical aluminum-plastic packaging blister was constructed. This model can be used to further achieve the classification and identification of pharmaceutical aluminum-plastic packaging blister of unknown categories at the scene of cases.