To explore the spatial distribution characteristics of soil total nitrogen content in forest farm and its response to environmental factors，random forest model and Cubist model were used to establish a quantitative relationship between soil total nitrogen content and environmental covariates including elevation，normalized difference vegetation index，mean annual precipitation，mean annual temperature，y-coordinate and aspect at soil depths of 0-10，10-30 and 30-50 cm in Wangyedian experimental forest farm. Soil total nitrogen content of the area was predicted and mapped，and the controlling factors affecting the spatial variation of soil total nitrogen were analyzed. The results showed that the average content of soil total nitrogen at soil depth of 0-10，10-30 and 30-50 cm was 3.20，2.02 and 1.47 g/kg，respectively. It decreased with the increase of soil depth. The results of cross-validation showed that the R² of the random forest model for predicting soil total nitrogen at the three soil depths was 0.59，0.42，and 0.39，respectively，better than the R² of Cubist model with 0.56，0.38，and 0.34，respectively. The prediction accuracy of both models decreased with the increase of soil depth. The influence of various environmental factors on the spatial distribution of soil total nitrogen decreased with the increase of soil depth. From the prediction map of soil total nitrogen with the random forest model，the content of soil total nitrogen at different soil depth showed a spatial pattern of low in the western，northern，and central regions，and high in the southwestern，southeastern，and eastern regions. The uncertainty map showed that the random forest model had a low standard deviation in predicting the distribution of the content of soil total nitrogen. The elevation had the greatest impact on the content of soil total nitrogen，followed by the normalized difference vegetation index>mean annual precipitation>mean annual temperature>y-coordinate>aspect. It is indicated that the random forest model can serve as an effective method for predicting the content of soil total nitrogen at different soil depth in the forest farm.