In this paper, soil particles in the Weigan River-Kuche River Oasis (referred to as the Wei-Ku oasis) were used as the research object, fifty typical surface (0 - 10 cm) soil samples were collected from the oasis, and environmental variables such as remote sensing index variables, topography and climate were extracted through relevant software. After correlation analysis to determine the relationship between environmental variables and prediction targets, a random forest (RF) model and an extreme gradient boosting (XGBoost) model for predicting soil particle contents were constructed using R language. The results show that the prediction results of the XGBoost model are better than those of the RF model, with the correlation coefficients ranging from 0.39 to 0.78. Soil pH, elevation and derivative variables, and spectral transformation variables are all important factors in the prediction of soil particle contents in both models. The errors of model prediction data are smaller than those of HWSD and measured data. In conclusion, the XGBoost model established by screening environmental variables is an effective method for predicting soil particle content in the Wei-Ku oasis.