China's high-speed railroads run long distances and have variable service environments, requiring high wheels performance. The grain size of the wheel directly affects the mechanical properties of the wheel, and the characteristics and measurement of the grain have an important role in materials science, so in order to ensure the safe operation of high-speed trains, it is necessary to test the grain size level of high-speed railway wheels. The laser-induced breakdown spectroscopy (LIBS) experimental platform was used to obtain the spectral information of five ER8 high-speed train wheel samples with different grain size grades (different grain size grades obtained after different heat treatments) by breakdown and the correlation between the intensity of the base element Fe and alloying elements (Cr, Mo, Co) and the five samples with different grain size grades was compared. Partial least squares-discriminant analysis (PLS-DA) models with the spectral line intensity as the variable were developed using this relationship, and standard normal variate transformation (SNV), multiplicative scatter correction (MSC). Savitzky-Golay convolutional smoothing methods were used to pre-process the models, respectively. The models were preprocessed by Standard normal variate transformation (SNV), Multiplicative scatter correction (MSC), Savitzky-Golay convolutional smoothing methods. By comparing various preprocessing methods, it is concluded that the effect of the model established after SNV preprocessing is the best. The number of misjudgments in the modeling set is 4, and the accuracy rate is 95.7%. The number of misjudgments in the prediction set is 3, and the accuracy rate is 90%.Based on the SNV preprocessing method, three-wavelength screening methods, competitive adaptive reweighted sampling (CARS), continuous projections algorithm (SPA), and CARS-SPA are selected for wavelength screening, and the model effects based on different characteristic wavelengths are compared. The results show that the model established after band screening with CARS has the best effect. The number of misjudgments in the modeling set is 2, and the accuracy is 97.9%. The number of misjudgments in the prediction set is 1, and the accuracy is 96.7%. The accuracy of the model is higher than 90%. Samples with different grain size grades can be classified. By comprehensively analyzing the results of the above discriminant analysis models, it is found that the PLS-DA model combined with SNV pretreatment and CARS band screening has the highest accuracy. The study shows that it is feasible to use laser-induced breakdown spectroscopy combined with partial least squares discrimination to analyze the grain size class of high-speed railway wheels, which can be used to evaluate the surface grain size class of wheels. Moreover, it provides a certain basis for applying LIBS technology to studying high-speed railway wheels with different grain size classes.