The elastic parameters of soft tissues are important for medical diagnosis and virtual sur-gery simulation.In this study;we propose a novel nonlinear parameter estimation method for soft tissues.Firstly;an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values.To provide highly precise data for estimating nonlinear param-eters;the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly;a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues;using the substitution parameters of Young's modulus and Poisson's ratio to avoid solving compli-cated nonlinear problems.To improve the robustness of our model and avoid poor local minima;the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally;a self-adapting Levenberg–Marquardt (LM) algorithm was presented;which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton;resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm;demonstrating that our nonlinear parameters are precise.