There is a large temperature difference between summer and winter in northern China. Soil temperature difference greatly influences the measurement of soil moisture by NIR (Near-Infrared). A prediction model for soil NIR spectrum and soil moisture content under a wide range of temperature stress (-20~40 ℃) was introduced. Soil samples were collected in the experimental field of Heilongjiang Bayi Agricultural University. After drying and sieving, soil samples were dampened to moisture content ranging from 15% to 50%. Prediction model for NIR and soil moisture content under different temperature stress was built. 69 groups of spectral data was used as training set to build model based on the full-band spectral data and five different spectral signal preprocessing methods. BP (Back-propagation) neural network, optimized support vector machine (SVM) algorithm and Gaussian process algorithm (GP) were used to establish the prediction model of soil near-infrared spectrum and moisture content in northern cold areas,and verify the effect of the model. The learning rate for BP neural network was 0.05, the maximum training time was 5 000, and the number of hidden layer units was 20. SVM used the radial basis function and Leave-One-Out Cross-Validation to determine the optimal penalty parameter (0.87), which improved the accuracy of the model prediction. Marton kernel internally was used for the GP model. GP model was evaluated by the coefficient of determination (R²), and root mean square error (RMSE). Results show that the S_G-BP neural network model has the best performance among the BP neural network models, with R² of 0.960 9 and RMSE of 2.379 7. The SNV-SVM model has the best performance among the SVM models with R² of 0.991 1 and RMSE of 1.081 5. The GP models, S_G-GP model has the best performance among GP models, with R² of 0.928 and RMSE of 3.258 1. In conclusion, the SVM model based on SNV preprocessing has the best training performance. 35 groups of spectral data were used as a prediction set to verify the model performance. According to the model comparison and analysis, the prediction model based on the SVM algorithm is better than the other two algorithms, among which the S_G-based SVM model has the best performance. R² and RMSE are 0.992 1 and 0.736 9, respectively. Combining the parameters of modeling set and prediction set, the SVM model based on S_G has the best performance in this study. This model can predict soil moisture content under a wide range of temperature stress in cold regions, providing a theoretical foundation for the design and optimization of portable NIR soil moisture rapid measurement instruments in the cold region.