Against the strategic backdrop of the nation’s efforts to boost consumption and expand domestic demand, internet-based credit payment, as a new payment method, has stimulated consumer demand while also intensifying the demand uncertainty faced by retailers in inventory decision-making. This paper aims to maximize retailer profit by constructing multi-period inventory decision models under two scenarios, namely with and without internet-based credit payment, so as to characterize the impact of internet-based credit payment on demand and ordering decisions. Furthermore, in order to address the uncertainty caused by demand fluctuations, a multi-period robust optimization model is developed based on a polyhedral uncertainty set, and duality theory is employed to transform it into an equivalent linear programming problem. Numerical simulations using JD Baitiao as a case study show that internet-based credit payment can effectively stimulate consumer demand, thereby encouraging retailers to increase order quantities and improve profits. In addition, a comparative analysis with the dynamic programming approach indicates that, while robust optimization sacrifices some degree of optimality, it significantly enhances the robustness of decision-making and effectively mitigates the impact of demand fluctuations on inventory decisions, thereby demonstrating greater stability and practicality in uncertain environments.