Corresponding to the internal chemical basic information of the fruit and the spectral information is the key to improve the model. For the current large thickness and a large volume of fruit, the visible/near infrared is poor in light transmission in the flesh region, the light refraction angle is difficult to determine, and the basic chemical information is not Accurate, resulting in poor quality prediction. In this paper, watermelon is the research object, and the intrinsic relationship between soluble solids and quality attributes in different regions of watermelon is discussed. Three hundred sixty samples are purchased in the fruit market. The parameters of the online detection device are divided into old parameters: integration time 100 ms, current 8.0 A and new parameters. The integration time is 150 ms, the current is 8.15 A to collect the watermelon spectrum, and the spectral absorption peak intensity is higher under the new parameters of the device. When the content of soluble solids in watermelon was determined, the watermelon was divided into 8 parts, and the heart sugar, medium sugar, peripheral sugar, base sugar (SSC) and mixed sugar (SSC) were determined respectively. The soluble solids in different parts of the pulp were large. The difference is that the most recent heart sugar value is the closest to the fruit center, and the lower the sugar value is, the closer it is to the melon skin area. Taking soluble solids in different regions of watermelon as the dependent variable, the new and old parameters of convolution smoothing (S-G) degraded spectral noise was used to establish a soluble solids partial least squares prediction model with independent variables, with 270 modeling sets and 90 prediction sets. The comparison model found that the new parameters improved the integration time of the sorting device and the tungsten halogen lamp current, which increased the prediction accuracy of the soluble solid model; while the soluble solids in the local area as the model’s dependent variable prediction effect was also higher than the mixed sugar. The model established by the variable, the bottom sugar region of the region close to the melon skin, due to visible/near-infrared incidence, and then a certain angle of refraction inside the melon, which indicates more watermelon fruit information, the modeling effect is the best, the prediction set the correlation coefficient is 0.89, and the root mean square error is 0.24. However, the internal information of watermelon fruit characterized by medium sugar and peripheral sugar region is less, and the modeling effect is poor. Therefore, the bottom edge of the watermelon is the best soluble solids collection area. This study reveals the characteristics of fruit light scattering and its intrinsic relationship with quality attributes, and realizes online updating of spectral database and analytical model.