In order to study the simulation effects of different land surface models on soil temperature in China, the Noah and Noah-MP land surface models are driven by the atmospheric driving data of the China Meteorological Administration (CMA) land surface data assimilation system (CLDAS) to simulate soil temperature in China. Soil temperature simulations of GLDAS_Noah, CLDAS_Noah and CLDAS_Noah-MP are evaluated from the perspectives of spatial distribution, different seasons, time series in different regions, etc. based on soil temperature observations collected at 2380 sites during 2010—2018 in China and the Noah soil temperature from the United States Global Land Data Assimilation System (GLDAS_Noah). This study realizes the comparative analysis of soil temperature simulated with different driving data, with same land surface models and same driving data, and with different land surface models. The results show that the GLDAS_Noah, CLDAS_Noah and CLDAS_Noah-MP can reasonably simulate spatial distributions of soil temperature at 10 and 40 cm-depth in China from a qualitative point of view, but there are certain differences in magnitude, which mainly occur in snow-covered areas of Northeast China, Xinjiang and Qinghai-Tibet Plateau. From a quantitative perspective, with the same land surface model and different driving data, CLDAS_Noah is better than GLDAS_Noah in different seasonal assessments based on bias spatial distributions and RMSE (Root Mean Square Error) time series in different regions. This result can indirectly show that CLDAS atmospheric driving data is better than GLDAS Atmospheric driving data and the atmospheric driving data is one of the important factors to improve the accuracy of soil temperature simulation. With the same driving data and different land surface models, the overall effect of CLDAS_Noah-MP is better than that of CLDAS_Noah. Among them, the errors of CLDAS_Noah winter soil temperature at 10 and 40 cm depths in snow-covered areas are significantly greater than that of CLDAS_Noah-MP, which may be related to the improvement of Noah-MP parameterization scheme in snow-covered areas. However, the spring soil temperature simulation errors of CLDAS_Noah-MP at 10 and 40 cm depths in Northeast, North China, and Qinghai-Tibet Plateau are significantly larger than that of CLDAS_Noah, which may be related to the snow melting parameterization scheme in the model. In short, this research provides certain references for subsequent development of soil temperature multi-model integration research and in-situ soil temperature data assimilation research and the final development of high-quality soil temperature dataset in China.