In order to establish a stable and reliable detection model to identify the quality of fresh jujube, the visible/near-infrared reflection spectroscopy techniques and the method of moisture compensation were used to detect the internal quality of fresh jujube. Moisture content (MC), soluble solid content (SSC), firmness, soluble protein content (PC) and vitamin C content (VC)were used as internal quality index of Huping Jujube, regression coefficient (RC) was applied to select effective wavelengths and least squares-support vector machines (LS-SVM) models were built based on the effective wavelengths, respectively. The results of the five RC-LS-SVM models were obtained with the determination coefficient of every prediction (R2P) of MC, SSC, PC, VC, firmness as 0.859 5, 0.884 0, 0.867 1, 0.909 9 and 0.826 1, respectively. The root mean square error of prediction (RMSEP) of MC, SSC, PC, VC, firmness were 1.243 1, 1.005 3, 3.324 9, 0.479 8 and 0.056 7, respectively. Then, wavelengths overlapped with or closed to characteristic wavelengths of moisture content were removed from characteristic wavelengths of SSC, PC, VC and firmness, respectively. Characteristic wavelengths of moisture content were composed of distinct moisture absorption peak on fresh jujube(960, 1 200, 1 400, 1 780 and 1 900 nm)and characteristic wavelengths selected by RC of PLSR model of moisture content. Characteristic wavelengths after the moisture compensation of each index (SSC, PC, VC, firmness) was used to carry out data fusion with moisture content of fresh jujube, moisture compensation LS-SVM model of each index (SSC, PC, VC, firmness) was built based on fused data, respectively. The results indicated that the model’s accuracy of firmness was improved after moisture compensation, Ｒ２P and RMSEP were 0.830 5 and 0.055 3, respectively. The results also revealed that the model accuracy of SSC, VC and PC were reduced respectively after moisture compensation, R2P were 0.804 1, 0.878 2 and 0.837 8, respectively and RMSEP were 1.347 3, 0.638 0 and 3.503 2 respectively. Finally, the correlation relationship between the quality indexes was analyzed. The results indicated that an significant correlation relationship was revealed between moisture content and firmness in the 0.05 level, an extremely significant correlation relationship was revealed between moisture content and any of the other three indexes (SSC, PC, VC) in the 0.01 level. This research shows that prediction model based on the method of moisture compensation can be effective to realize evaluation of the internal comprehensive quality on Fresh Jujube. What’s more, there is an interaction between moisture content and any of the other four indexes. In fact, prediction models based on the other quality indexes are affected by moisture content. This research provides a new method for the decoupling of interaction between the various internal quality indexes in the spectroscopy detection.