NIR Hyperspectral imaging technology was used to detect soybean moisture content rapidly and non-destructively and realized the visualization of soybean moisture content. A total of 96 soybean samples of hyperspectral images in the region of 900~2 500 nm were acquired, and the moisture content of each soybean sample was measured by the direct drying method. The average spectral information of the region of interest(ROI)of the image was extracted by HSI Analyzer software, representing the sample's spectral information. The SPXY algorithm was used to divide the sample calibration set and prediction set, and the spectral data in the band range of 938 to 2 215 nm were retained. The spectral's pretreatment was analyzed, such as Moving Average, Smoothing S-G, Baseline, Normalize, Standard Normal Variate(SNV), Multiple Scattering Correction(MSC)and Detrending, and the PLSR model established after Normalize pretreatment had the best effect. The characteristic wavelengths were selected by successive projections algorithm(SPA), competitive adaptive reweighted sampling(CARS)and uninformative variable elimination(UVE). 14,16 and 29 characteristic wavelengths were selected by SPA, CARS and UVE, accounting for 6.5%, 7.4% and 13.4% of the total wavelengths. The prediction models were established for the spectra and characteristic wavelengths of 938~2 215 nm, and the model with better effect was combined with the Normalize method. Compared with the 14 prediction models established, it was found that the modeling and prediction effect of characteristic wavelengths selected by the SPA algorithm was good, and the Normalize-SPA-PCR model was optimized. The values of RC2 and RP2 in the model were higher, which were 0.974 6 and 0.977 8, respectively, while the values of RMSEP and RMSECV in the model were lower, which were 0.238 and 0.313, respectively. The stability and predictability of the model were good, which could be used to predict the soybean moisture content accurately. The Normalize-SPA-PCR model was used as a visual prediction model for soybean moisture content, and the moisture content of each pixel in the hyperspectral image was calculated to obtain a gray image. The gray image was transformed by pseudo-color transformation to obtain a visual color image of soybean moisture content. The 24 soybean varieties in the prediction set were visualized. The color of the visualized image was different with different moisture content, and the color of the visualized image was more evident with different moisture content. The results showed that hyperspectral imaging combined with stoichiometry could accurately, rapidly, and non-destructive predict soybean moisture content. They realized the visualization of soybean moisture content, which provided technical support for soybean moisture content detection in the process of soybean harvest, storage and processing.