The worse convergence rates in the stochastic case compared to the deterministic case pose challenges in accurately modeling and simulating stochastic heat equations. This discrepancy is primarily due to the inherent randomness in the system, which introduces additional complexities and uncertainties that affect the convergence behavior of numerical schemes. To address this issue in future research, advanced stochastic analysis techniques and probabilistic methods could be employed to better understand the behavior of stochastic heat equations. Developing novel numerical algorithms specifically tailored for stochastic systems, such as stochastic Galerkin methods or probabilistic collocation techniques, could help mitigate the impact of the slower convergence rates. Furthermore, exploring hybrid approaches that combine deterministic and stochastic methods could offer a promising direction to improve convergence rates and computational efficiency in stochastic heat equation simulations.