Abstract: Tea grade evaluation is a systematic work with complex and subjective. Grade information from its relevant quality data extracted enables to establish rapid identification method, later of which has guiding meaning to tea production. To establish a rapid identification method of white tea grades, 200 white tea (Bai Mudan) samples with 4 grades were selected as the research objects in this paper, near infrared spectroscopy and gas chromatography-ion mobility spectrometry were used to collect original data. The data dimensions were reduced by principal component analysis or linear discriminant analysis, combed with 7 data mining classifier algorithms to rapidly evaluate the grades of white tea. Results showed that linear discriminant analysis was suitable for dimensionality reduction of the original data from near infrared spectra and gas chromatography-ion mobility spectrometry. After dimensionality reduction of the original data using linear discriminant analysis, classification algorithm including adaptive boosting (Adaboost), k-nearest neighbor (KNN) and multi-layer perceptron (MLP), and random forests (RF), stochastic gradient descent (SGD) and support vector machines (SVM) were used for establishment of white tea grade discriminant models based on near infrared spectroscopy, the correct rate of these models were greater than 94%, and the AUC of the model evaluation index was≥0.95. The discriminant rates of MLP, SGD and SVM models based on gas chromatography-ion mobility spectrometry filtered spectrum data were 91%~93% and the AUC value were 0.94~0.96. The positive judgment rate of models from Adaboost, decision tree (DT), KNN, MLP, SGD and SVM models based on gas chromatography-ion mobility spectrometry labeled substance data was 100%, and the AUC was 1.0, while the model evaluation index of RF model were 96% and 0.98, respectively. With near infrared spectrum and volatile compound characteristic data as important parameters for white tea grade evaluation, 6 and 10 kinds of grade discrimination models were built, which could accurately determine the grade of white tea, and the classifier algorithm was suitable for the modeling of these two types of data.