The establishment of soil total nitrogen content estimation model based on hyperspectral data can provide a new technology and method for rapid and accurate monitoring of soil total nitrogen content in farmland，judging crop growth and development and evaluating land quality. Taking the main types of soil in southern Xinjiang as the research object，soil total nitrogen content and spectral reflectance data are determined by indoors，using partial least square regression (PLSR)，support vector machine regression (SVM)，random forest regression (RF)，the spectral reflectance (R) and their mathematical transformation to establish an estimation model for predicting the total nitrogen content of the whole district and subregion.The results show that the prediction set R2 and RPD of the PLSR optimal model are 0.73 and 1.82，respectively； the prediction set R2 and RPD of the SVM optimal model are 0.75 and 1.97，respectively； and the prediction set R2 and RPD of the RF optimal model are 0.86 and 3.52，respectively； the prediction abilities of the three models are in the order of RF>SVM>PLSR.Except for the first-order differential (FD) transformation，other data transformations have different degrees to improve the model accuracy.The R and its four transformed data have high modeling accuracy with RF，while the modeling accuracy with PLSR and SVM is relatively low.The stability of the whole region model is higher than that of the partition model and the partition model has obvious difference and poor stability.On the whole，RF model has stable predictive ability，good applicability and high accuracy，and can accurately estimate the total nitrogen content of the soil.However，PLSR and SVM models can only make rough estimates of total nitrogen content.RF model can be used to estimate the total nitrogen content of soil in the study area quickly and accurately.