Machine olfaction is an emerging bionic technology based on sensor arrays and computer algorithms to simulate biological olfaction. The characterization of odor substances is a field worthy of research in machine olfaction. At present, olfactory perception is in the preliminary research stage, and the general classification theory of odor is not yet mature. . In this paper, starting from the electronic information of material odor, aiming at the relatively balanced fragrance data in the collected data, using machine learning algorithms and parameter adjustments, grid search and other model optimization methods, the material odor classification model based on electric nose data is proposed, and the connection between the information and perception of the material odor electronic nose is established. The experimental results show that the random forest model performs better than other machine learning algorithms in each evaluation index, and the average accuracy of odor classification based on random forest reaches 93.6%.