The difficulty of sharing detection models built by different instruments is common in the quality inspection and classification of fruits. In the study, the “Huping” jujube was used as the research object, and the transfer method of the soluble solids content (SSC) detection model between instruments was explored using visible/near infrared spectroscopy. First, the spectral information of samples was collected using two instruments produced by American Analytical Spectral Device. Based on the original, Savitzky-Golay first derivative processed and standard normal variable transformed spectrum, SSC detection models were established by least squares-support vector machines (LS-SVM), respectively. The prediction ability of the three methods for the spectra acquired by different instruments was poor. The built model by the original spectrum of the master instrument was optimal in predicting spectra from the same instrument. The determination coefficient (R2p) and the root mean squared error of prediction (RMSEP) were 0.73 and 1.36%, respectively. Next, the Kennard/Stone algorithm was used to select standard samples. The Shenk’s, direct standardization (DS) and slope/bias (S/B) algorithm were used for model transfer, respectively. Then, according to the regression coefficient, the sensitive wavelengths of the master instrument (24) and the slave instrument (28) were extracted. 24 single variables (SV), 23 common variables (CV) and 29 fusion variables (FV) were selected, all of which contained the main absorption bands of SSC. LS-SVM detection models of the master instrument were respectively established by the preferred variables, which (R2p=0.78~0.80, RMSEP=1.07%~1.13%) was better than the model built by the full wavelength for the prediction result of the master instrument. However, the model failed in predicting spectra from different instruments (RMSEP=6.62%~7.88%). Finally, based on the wavelength position shift and the absorbed property of molecular vibration, these algorithms named as common variable-subtraction correction (CV-MC), single variable- subtraction correction, fusion variable-subtraction correction and common variable-wavelength correction (CV-WC) were respectively proposed for model transfer. These methods were compared with SV-Shenk’s, CV-Shenk’s, FV-Shenk’s, SV-DS, CV-DS, FV-DS, SV-S/B, CV-S/B and FV-S/B algorithms. The results showed that the prediction results (R2p=0.03~0.34, RMSEP=2.44%~4.67%) were poor when the model was transferred by the full-band. Using the model built by the preferred variables, the results transferred by SV-Shenk’s, CV-Shenk’s and FV-Shenk’s were poor, and the results transferred by other algorithms (R2p=0.47~0.73, RMSEP=1.30%~1.90%) were better than the full wavelength. The CV got better transfer results than the SV and the FV, and the CV-MC result was the best (R2p=0.73, RMSEP=1.30%). The predicted result after CV-WC transfer (RMSEP=1.62%) was similar to CV-DS and CV-S/B. The research indicates that both CV-MC and CV-WC are effective model transfer algorithms, which are of great significance to establishing a common jujube quality detection model between different instruments.