This paper constructs a commercial credit supply decision-making model based on the analysis of corporate accounts receivable. The regression analysis algorithm is used to categorize and calculate the variable parameters affecting credit supply, and the expectations of suppliers and vendors are used as the predicted value for decision-making. The BP neural network is used to assess the risk of business accounts receivable from the horizontal as well as vertical perspectives, respectively, and to enhance the security quality of credit supply decision-making. The results show that strengthening the management of accounts receivable enhances the robustness of corporate accounting, keeps the rate of change in surplus around 0.4% per year, and the accounts receivable turnover rate reaches a maximum of 15.9 times/year so that the business credit supply decision will be more prudent.