With the rapid expansion of the railway scale, the requirements for the reliability and durability of train operation are getting higher and higher. As the core component of the railway vehicle system, the friction between the wheel and the track must ensure safety and increase the speed. The performance of the wheel material directly affects the sensitivity of the wheel to wear and rolling contact fatigue damage, and its service performance is also highly concerned. Studies have shown that the composition and distribution of wheel steel materials can significantly impact the performance of its microstructure.Therefore, this paper aims to use the laser-induced breakdown spectroscopy technology to quickly analyze the high efficiency of multi-element, better spatial resolution, scanning analysis capabilities in a larger area and other technical advantages, combined with statistical distribution analysis method, to achieve rapid characterization of the composition and distribution of wheel steel materials. In this paper, the vertical surface of the wheel rim was selected as the analysis surface. The low time's test showed that there were obvious thick dendrite structures in thearea away from the tread surface, and the organization structure had unevenness, and use this as a feature analysis area for sampling. 320 mesh alumina sandpaper was used for surface treatment, and the LIBSOPA system was used for component distribution analysis. First, under different ablation conditions, the spectral signal intensity and stability of each element's characteristic spectral line were compared and analyzed, and 20 pre-ablation and 10 ablations were optimized as experimental conditions; second, using established the standard internal method to characterize the quantitative results of nine elements such as Si, Mn, P, S, Cr, Ni, Mo, Cu, V in wheel steel. The quantitative results and the results of direct-reading spectrum analysis have good consistency; In the end, the sample was scanned regionally, and the statistical distribution of each element's composition distribution was statistically characterized. The statistical results of the composition distribution partition showed that the statistical segregation degree of all elements near the tread area was less than that away from the tread area. Based on the statistical segregation degree and the two-dimensional distribution map of the components, it can be seen that the distribution of the components of the test sample away from the tread area is uneven, and the results correspond well with the results observed by the low-times test method. In this paper, the LIBSOPA technology is used to realize the composition distribution characterization of multi-element in the train wheel steel material, which provides a new idea and characterization method for quickly determining the composition and distribution state of the wheel steel material.