Abstract: To model the nonlinear system and get rid of the limitation of existing nonlinear partial least squares (PLS), a nonlinear PLS algorithm based on the transformation of fuzzy basis functions is presented. The nonlinear relationship between independent and dependent variables are changed into linear one by transforming the fuzzy basis functions. Then, the PLS algorithm is used to get the regression parameters of the transformed independent and dependent variables so as to efficiently solve the problems of dimension increasing and multi-collinearity caused by the transformation of fuzzy basis functions. The model thus developed has the goodness of fit and is available to restrain the noise in process data. Simulation test results verified the superiority of this method to other nonlinear PLS methods, and it has been applied to the soft sensing modeling for component contents in hydrometallurgy extraction process with satisfactory prediction results obtained.