Abstract: In order to solve the complex optimization problems in coal blending decision-making process, an optimization model of the coal blending decision-making system is constructed by introducing particle swarm optimization (PSO) and simulated annealing (SA) algorithms, so as to improve the characteristics of coal blending and optimize the utilization of resources, and then to realize the enhancement of economic benefits. Taking Haragou coal mine in Shenmu County, Shaanxi Province as an example, two kinds of coals, Shenyou 2 and extra-low ash, are selected as blending objects. First, based on PSO and SA algorithms, the optimization model of coal blending decision-making system is constructed. By comparing and analyzing the coal quality characteristics under different blending scenarios, including key indexes such as ash, total moisture, and heat content, the advantages and disadvantages of each scenario are evaluated. Secondly, the downgrade sales ratio of blended coal before and after optimization was calculated using actual sales data to assess the impact of the optimization model on resource utilization. Finally, the optimization effects of different algorithms (SAPSO, SA, PSO) under the adaptation value were compared to verify the superiority of SAPSO algorithm. The results show that the optimized blending scheme using PSO and SA algorithms makes the ash, total moisture, heat content and ash interval of the two blended coals, Shenyou 2 and extra-low ash, meet the relevant requirements, and the stability of the coal quality interval is significantly improved. After optimization, the downgrade sales of both extra-low ash and Shenyou 2 blended coals are lower than the requirements, which effectively improves the resource utilization rate and reduces unnecessary expenses. SAPSO algorithm shows better optimization effect than SA and PSO algorithms, and it can more accurately reflect the change trend of the model. By introducing PSO and SA algorithms, the optimization model of coal blending decision-making system is successfully constructed, which effectively improves the coal quality characteristics and resource utilization of blended coal. SAPSO algorithm shows excellent optimization effect, which provides a strong theoretical support for coal blending decision-making. The study not only has theoretical significance, but also realizes significant economic benefits by optimizing the blending scheme, which provides a useful reference for the sustainable development of the coal industry.