In order to enhance the adaptability of the near-infrared model，a near-infrared spectral model transfer method based on sparrow search algorithm combined with deep feedforward neural network（SSA－DFN） was proposed in this paper，aiming at the problem that the function transfer relationship between master and slave is difficult to determine due to the nonlinear interference caused by the difference between different stations and environmental factors．The depth feedforward network was used to fit the nonlinear function mapping between spectra collected with different instruments，and the sparrow search algorithm was used to initialize the connection weights and thresholds of each layer of the network．The optimal initial values of the connection weights and thresholds were obtained through iterative updating of individual positions in the population．By adjusting the super parameters of the depth feedforward neural network model many times，the network fitting effect tends to be optimal，and finally the transfer function is determined．To verify the effectiveness of this method，SSA－DFN method was compared with PDS and CCA before and after the transfer from the perspectives of near infrared spectrum，principal component projection and prediction results．The results showed that SSA－DFN method has the highest coincidence degree between the slave spectrum after transfer and the original host spectrum，and there is no significant difference in the prediction results of total sugar and nicotine content between the master and slave after transfer．The average prediction error decreased from 8.32% and 9.15% to 4.65% and 4.82%，respectively．The indexes such as RMSEP and ,R,2, were better than PDS and CCA，which achieved the best transfer effect and met the needs of enterprises，indicating that this method is an effective model transfer method.