Determining the parameters of constitutive models is typically a material-specific process that requires recalibration when the material changes. This procedure becomes notably time-consuming with an increased number of parameters and integral terms in the constitutive equations. Recently, a novel approach utilizing neural networks has emerged as an alternative. By training neural networks to replace constitutive equations, constitutive substitution method maps the extensive parameter space to computational results equivalent to those derived from original equations. Comparing these results with experimental data allows for efficient determination of optimal parameter vectors. The powerful fitting capabilities and rapid computational speed of neural networks significantly expedite the parameter determination process. Consequently, the task of solving for parameters transitions from a complex optimization problem to a straightforward computational search. In this work, we introduce an iterative parameter generation (IPG) algorithm to enhance this substitution method, thereby improving its ability to fit a diverse range of materials. Additionally, we explore the benefits of constructing a general parameter space applicable to various material classes.