Most existing neural network models designed for solving time-varying quadratic programming (TVQP) problems exhibit limitations in convergence performance and stability, significantly constraining practical implementation in engineering applications. To overcome these challenges, this article proposes an adaptive zeroing neural network model based on fuzzy factors(AFFZNN). Theoretical analysis rigorously establishes the convergence performance of the proposed AFFZNN model under time-varying conditions. Numerical simulations further demonstrate superior convergence speed and steady-state accuracy compared to other enhanced ZNN models, collectively validating the model’s effectiveness for solving TVQP problems.