In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in energy-efficient state. In the proposed scheme, production indicators prediction models are firstly developed using kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multi-objective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which, an improved association rules mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility.