Abstract: In order to rapidly determine difference between biochars produced at different temperatures in property, partial least squares (PLS) and least squares support vector regression (LSSVR) were used to predict properties of the biochars produced out of corn stalk through pyrolysis at 300, 400, 500 and 600℃ and then principal component analysis (PCA) and linear discrimination analysis (LDA) were done of the biochars to determine which one was pyrolyzed at what temperature. Prediction and analysis of the biochars for properties such as contents of ash, fixed carbon, carbon (C), and hydrogen (H) was performed using a near-infrared spectroscopy coupled with several statistical approaches, with root mean squares error of prediction (RMSEP) being 0.024 2, 0.029 9, 0.016 9 and 0.001 7, residual predictive deviation (RPD) being 1.32, 3.50, 3.44 and 5.66, and corresponding coefficients determination (R²) being 0.974 2, 0.912 6, 0.912 5 and 0.927 4, respectively. Moreover, the determination of temperature at which the biochars had been pyrolyzed was done successfully with 100% accuracy with LDA. Results show that compared with the wet chemical method, near-infrared spectroscopy features rapidity and non-destructiveness in analyzing biochar properties. The proposed method is of great significance to wide application of biochar.