In the smart grid, the development of electric power Demand Response (DR) brings great change to the traditional power utilization mode. Combined with real-time electricity price, consumers can adjust their power utilization mode by their energy demand. This makes load forecasting more complicated. The multi-input and two-output Least Squares Support Vector Machine (LS-SVM) was proposed to preliminarily predict the load and price at the same time. Considering the interaction between the real-time electricity price and load, the fuzzy recursive inference system based on data mining technology was adopted to simulate the game process of the forecasting of the price and load, and then the preliminary forecast results of multi-variable LS-SVM prediction algorithm were recursively corrected until the forecasting results were tending towards stability. Multi-variable LS-SVM can avoid running into local optima and has an excellent capacity of generalization, the improved association rules mining algorithm and loop predictive control algorithm have good completeness and robustness, and can correct the forecasting result approximately in every real situation. Simulation results of the actual power system show that the proposed method has better application effects.