Soil nutrient status is directly related to crop yield and quality. However, traditional chemical methods have problems such as large consumption of chemical reagents, being time-consuming and labor-intensive, and cannot meet the needs of precision agriculture. Quickly obtaining soil nutrient information is the key to developing precision and green agriculture. To understand soil fertility, one must first understand the content of organic matter and total nitrogen. Many studies have shown that near-infrared spectroscopy is widely used in soil detection, but visible/near-infrared spectroscopy is very rare in the study of soil organic matter and total nitrogen. Taking four villages in Anfu County, Ji’an City, Jiangxi Province, and Xinjian District, Nanchang City as the study areas, the three most typical soil samples, brown soil, red soil and paddy soil, with a depth of 10~30 cm were collected according to the 2×2 grid method180 share. After grinding, air-drying, etc., the samples were divided into two parts by the method of quartering, which was used to determine the samples’ spectral and physicochemical information. The soil samples were divided into modeling set and a prediction set according to 2∶1 (120∶60). Considering the large noise in the first-end band, the 325~349 nm and 1 051~1 075 nm bands were removed the remaining 350~1 050 nm band was used for spectral analysis. 12 wavelength points of OM and 11 wavelength points of TN were screened out by successive projections algorithm. Considering the possible nonlinear relationship between soil spectral information and soil physical and chemical properties, a full-band, the linear partial least squares regression (PLSR) model of characteristic wavelengths and the nonlinear least squares support vector machine (LS-SVM) model were used to study soil organic matter and total nitrogen. The LS-SVM model was optimized by a two-step grid search method. Two hyperparametersγ and σ2. The results show that: (1) The spectral reflectance of soil increases with the increase of wavelength, and the reflectance curve has obvious absorption characteristics at 460, 550, 580, 740 and 900 nm. (2) From the analysis of the results of the PLSR model and the LS-SVM model, it can be seen that the nonlinear model LS-SVM has better prediction accuracy, which may be due to the nonlinear relationship between soil spectral information and soil physical and chemical properties. (3) The characteristic wavelength screened by the continuous projection algorithm improves the model accuracy and optimizes the model operation efficiency. The SPA-LS-SVM model was the best predictive model among all the models, among which the R2pre of the organic matter model was 0.884 7, the RMSEp was 0.104 8, and the RPD was 2.945 0. The R2pre of the total nitrogen model was 0.901 8, the RMSEp was 0.010 4, and the RPD was 3.191 1. (4) This study shows that visible/near-infrared spectroscopy can measure different types of soil organic matter and total nitrogen content, achieving better prediction results. Visible/NIR spectroscopy has great potential in the field of soil detection.