Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small thin-peel fruit, although less so for large thick-peel fruit because of the low signal-to-noise ratio of the spectral signal, resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. This research explored a method of spectroscopy for the total soluble solid (TSS) content and acidity detection of ‘Shatian’ pomelo, which are two major parameters of fruit internal flavor. VIS/NIR spectral signal detection of 100 pomelo samples during storage was performed. Detection based on raw data, signal jitter, and scattered light noise removal, feature extraction, and deep learning were performed and combined with modeling detection to achieve an accurate step-by-step detection. Our results showed that 600 W is the optimal light intensity for detecting the internal flavor of pomelo. The TSS content of pomelo is optimally detected using Savitzky-Golay (SG) + multiplicative scatter correction (MSC) + genetic algorithm (GA) + principal component analysis (PCA) + convolutional neural network (CNN) + partial least squares regression (PLSR); however, acidity of pomelo is optimally detected using SG + MSC + GA + PLSR. With the optimal detection method, the coefficient of determination and root mean squared error (RMSE) of the validation set for TSS detection are 0.72 and 0.49, respectively; and for acidity detection are 0.55 and 0.10, respectively. Even though the accuracy is not high, the data are still acceptable and helpful in nondestructive quality grading of large quantities postharvest fruit. Therefore, our results demonstrated that VIS/NIR was feasible for detecting the TSS content and acidity of postharvest pomelo, and for providing a possible method for the nondestructive internal quality detection of other large thick-peel fruit.