The objective of this study is to explore the optimal detection location and the best prediction model of the suger level of Yongquan honey oranges, which can provide a theoretical basis for the brix measurement and classification of honey oranges. With the wavelength range of 390.2-981.3 nm hyperspectral imaging system was used to study the best position for detecting the sugar content of Yongquan honey oranges, and the spectral information of the calyx, fruit stem, equator and global of Yongquan honey oranges were combined with their sugar content of corresponding parts to establish its prediction model. The original spectra from the different locations were pre-processed by Standard Normal Variance (SNV) transformation, Multiple Scattering Correction (MSC), baseline calibration (Baseline) and SG smoothing, respectively, and the Partial Least Squares Regression (PLSR) and Least Squares Support Vector Machine (LSSVM) models were established based on the pre-processed spectral data. The best pre-processing methods for different parts of the honey oranges were found, and the optimal spectral data obtained by the best pre-processing methods were conducted to identify characteristic wavelengths using the Competitive Adaptive Re-weighting Sampling algorithm (CARS) and Uninformative Variable Elimination (UVE). Finally, the PLSR and LSSVM models were established and compared based on the selected spectral data. The results show that the global MSC-CARS-LSSVM model demonstrates the most accurate prediction performance, with a correlation coefficient of Rp=0.955 and an RMSEP value of 0.395. Alternatively, the SNV-PLSR model of the equatorial location of honey oranges was found to be the next more effective, with a correlation coefficient of Rp=0.936, and an RMSEP value of 0.37. The correlation coefficients of the two prediction models are similar, the equatorial location can be used as the optimal position for measuring the sugar content of honey oranges. This study demonstrates that the prediction models based on different parts of the orange have different effects. Identifying the optimal position and prediction model can provide a theoretical basis for classifying oranges for sugar content testing.