The blast furnace ironmaking process, as a crucial part of modern steel production, directly affects the economic benefits of steel enterprises in terms of operational stability and production costs. However, due to the influence of uncertain factors such as fluctuations in raw material composition, operational conditions and production conditions, there is a significant amount of randomness within the economic objective function to be optimized. Under such circumstances, traditional optimization methods based on the mean value of the objective function are difficult to achieve satisfactory optimization results. Therefore, this paper proposes an economic stochastic optimization method for the blast furnace ironmaking process based on the distribution shape of the objective function. Firstly, considering the internal uncertainty of the objective function that characterizes economic costs, the kernel density estimation is used to describe the distribution shape of the objective function, and the random uncertainty during the optimization process is described through the probability density function (PDF) shape of the objective function. Then, we construct performance indicators for both the error between the PDF of the objective function to be optimized and the expected PDF, and for entropy optimization under mean value constraints. By optimizing the distribution shape of the objective function, the randomness during the optimization process is minimized. Finally, the effectiveness and superiority of the proposed method are verified through experiments based on real data from the blast furnace ironmaking process.