Abstract: To address energy optimization for residential buildings equipped with both fresh air systems and air conditioning systems, a multi-objective optimal control strategy for fresh air-air conditioning system is proposed based on the dynamic knowledge graph of fresh air-air conditioning system. First, expert knowledge and real-time monitoring data were fused to establish a dynamic knowledge graph model of fresh air-air conditioning system, and the candidate sets of fresh air volume, air conditioning set temperature, and air conditioning set air speed were constructed based on the model. Second, the candidate sets were used as the constraint condition of the multi-objective optimization algorithm, and the optimization goals of minimizing the overall system energy consumption and maintaining optimal indoor comfort were established. Third, the logistic chaotic mapping, golden sine strategy, and polynomial variation strategies were used to improve the multi-objective whale optimization algorithm(MOWOA), and the improved multi-objective whale optimization algorithm(IMOWOA) was used to optimize the fresh air volume, air conditioning set temperature, and set air speed in real time to converge to the optimal solution, effectively balancing the conflicts between energy consumption and comfort, and achieving coordinated control of the fresh air system and air conditioning system. Finally, the proposed strategy was validated in a highly simulated residential environment simulation platform, which reduces the building power consumption by about 3.98% and improves the solution speed by 36.8%. Results show that the control strategy of this paper ensures indoor environmental comfort while reducing the overall system energy consumption, significantly improves the efficiency of the optimization algorithm iteration, and achieves the goal of reducing costs and increasing efficiency to a good extent.