The sugar content of peanut seeds is an important indicator that affects the quality of eating. Establishing a method for rapidly determining sugar content can effectively improve the detection efficiency of edible peanuts. The appearance color of the sample is one of the important factors that affect the near-infrared analysis. Classification and correction according to the appearance color of the sample are more conducive to improving the model’s the model’s predictive performance. Therefore in this study, 332 peanut germplasms with different sugar content were selected, and the peanut germplasms were divided into three categories: black-purple, red, and pink, according to the seed coat color using a colorimeter. The 3,5-dinitrosalicylic acid, anthrone colorimetric, and sucrose enzymatic methods were used to determine the total sugar, soluble, and sucrose content in seeds, respectively. The total sugar content was 6.42%~39.53% (blackpurple peanuts), 9.66%~39.71% (red peanuts), and 8.52~38.84 (pink peanuts). The soluble sugar content was 2.4%~14.32% (blackpurple peanuts), 2.94%~13.75% (red peanuts), 2.19%~14.53% (pink peanuts), and the sucrose content was 0.92%~7.53% (blackpurple peanuts), 1.05%~7.23% (red peanuts), 0.95%~7.99% (pink peanuts), the coefficient of variation was above 33%. The Perten DA7250 near-infrared analyzer (950~1 650 nm) was used to collect the near-infrared spectrum values of the seeds. The total 9 NIR spectroscopy calibration models about the total sugar content, soluble sugar content, and sucrose content of blackpurple, red, and pink peanut seeds were established respectively through the Partial Least Square Regression (PLSR) method based on the whole band, and the single or compound pretreatment methods, and comparing the correlation coefficient and error of the models. The correlation coefficient ofcorrection (R_c) was 0.883~0.925, and the root means standard error of calibration (RMSEC) was 0.370~1.988. In the model of total sugar content, the R_c of pink seed coat peanuts was 0.925, and the RMSEC was 1.705. In the model of soluble sugar content, the R_c of blackpurple seed coat peanuts was 0.921, and the RMSEC was 0.667. In the model of sucrose content, the R_c of blackpurple seed coat peanuts was 0.914, and the RMSEC was 0.435. External verification was carried out using 15 germplasms. The correlation coefficient of prediction (R_p) of the 9 models was 0.892~0.967, and the root means standard error of prediction (RMSEP) was 0.327~2.177. In this study, near-infrared models could predict the content of several sugars in peanut seeds simultantly and rapidly, and provide technical support for ediblepeanut breeding of high sugar content.