The work aims to explore the feasibility of machine learning algorithms in the quick identification of composite packaging film materials with inspection big data. 1333 samples of ten composite packaging films with different numbers of composite layers, different functional layer materials, and different food contact layer materials were used as data sets and the inspection values of tensile strength in toughness direction, tensile strength in rigid direction, elongation at break in toughness direction, elongation at break in rigid direction, water vapor transmission rate, oxygen transmission rate, and thickness were used as characteristic values. Then, the machine learning algorithms of artificial intelligence were used to identify the materials of composite films. After comprehensively comparing the six learning algorithms of Decision Tree, Logistic Regression, SVM, K Neighbors, MLP, Gaussian Naive Bayesian, the Decision Tree algorithm was found to have high accuracy, kappa coefficient and running speed. After parameter optimization, the accuracy and kappa coefficient of Decision Tree algorithm for material identification were 95.4% and 93.2%, respectively. Therefore, the Decision Tree algorithm has a certain advantage in the identification of composite packaging film materials.