In view of the limitations of S/B algorithm for nonlinear problems in calibration model transfer, based on the traditional S/B algorithm that uses linear fitting and partial least squares method for parameters, this paper improved it by introducing high power of variable and using Lagrange and Newton interpolation method to seek undetermined coefficients and the interpolation polynomial to solve the nonlinear problem of the two sets of data. In order to verify the validity of the improved algorithm, this paper built a model for the master machine firstly and predicted the master and slave machine samples respectively, and then through the experiment data and the valuation index, it selected the best function relation to correct the model and finally predicted the unknown samples of the slave machine with the improved S/B algorithm and the traditional S/B algorithm. Experimental results showed that the gap is larger between reference value and the predicted value with master model directly, the predicted value with improved S/B algorithm was closer to the reference value than the traditional S/B algorithm. The improved S/B algorithm enhanced the accuracy of the prediction and solved the nonlinear problem of the traditional S/B algorithm. The algorithm based on Lagrange and Newton interpolation achieved better effect of model transfer and enhanced the generality of application in network modeling.