As a result of global climate change and human production activities, algal blooms are occurring in aquatic environments. The problem of eutrophication in water bodies is becoming increasingly severe, affecting the safety of drinking water sources. In this study, an algal bloom risk index model combining the Improved Fuzzy Analytic Hierarchy Process (IFAHP), Entropy Weight Method (EWM), and Game Theory (GT) was proposed for the Shanxi Reservoir based on the TOPSIS method. After the seasonal and spatial variability in algal bloom risk from 2022 to 2023 was analyzed, an adaptive simplification of the algal bloom risk index calculation was proposed to optimize the model. To enhance its practical applicability, this study proposed an adaptive simplification of the algal bloom risk index calculation based on an improved TOPSIS approach. The error indexes R² for the four seasons and the annual analysis were 0.9884, 0.9968, 0.9906, 0.9946, and 0.9972, respectively. Additionally, the RMSE, MAE, and MRE values were all below 0.035, indicating the method’s high accuracy. Using the adaptively simplified risk index, a risk grading and a spatial delineation of risk areas in Shanxi Reservoir were conducted. A comparison with traditional risk classification methods showed that the error in the risk levels did not exceed one grade, demonstrating the effectiveness of the proposed calculation model and risk grading approach. This study provides valuable guidance for the prevention and control of algal blooms in reservoir-type drinking water sources, contributing to the protection of drinking water sources and public health.