Based on the data of 205 sample points of soil series survey in Qinghai Province in recent years, the quantitative relationships between the contents of total nitrogen (TN), total potassium (TK) and total phosphorus (TP) of topsoils (0-20 cm) and environmental factor variables (terrain, climate, vegetation and remote sensing data) were established respectively by using random forest model, and the spatial distribution of soil nutrient contents in Qinghai Province was predicted, then the management zoning of soil nutrients was generated by using the projection pursuit method and the national soil nutrient classification standard. The cross validation results show that R² of spatial prediction of TN, TK and TP are 0.89, 0.85 and 0.82, respectively. The model can explain more than 80% of the spatial variation of soil nutrients, indicating that the combination of random forest model and environmental factor variables can effectively predict the spatial variation of soil nutrients in large-area alpine mountainous areas under the condition of sparse samples. The distribution pattern of soil nutrients in Qinghai Province is high in the east and low in the west. The high levels of soil comprehensive nutrients appear in Yushu, Guoluo, Huangnan in the south and Huangshui Valley in the east; The lower grades are mainly distributed in Qaidam Basin, Hoh Xil and the south central part of Hainan prefecture; The soil nutrient classification of the whole province is above the middle and upper grades, accounting for 81% of the total area of the whole province. It is found that vegetation is the main environmental factor affecting the spatial distribution of soil nutrients in topsoil in Qinghai Province, among which annual precipitation and surface temperature are important factors affecting the spatial model of TN in topsoil in Qinghai Province. The spatial variation of TP in topsoil was dominated by environmental factors such as surface cover, altitude and surface temperature. Annual precipitation and temperature difference between day and night are important factors affecting the spatial model of TK in topsoil.