Building integrated energy system is an effective way to achieve low-carbon buildings. In order to further tap into its demand side adjustable potential and carbon reduction potential, and reasonably allocate the interests of various entities in the building integrated energy system, a two-layer optimization scheduling strategy for building integrated energy system considering virtual energy storage in buildings under the stackelberg game is proposed. In the game model, the thermal inertia of the cooling or heating system inside the building and the flexibility of the cooling or heating load are considered to leverage the virtual energy storage function of the building and improve system flexibility. Using genetic algorithm to solve the pricing model of upper level energy operators, updating the purchase and sale electricity prices of upper level leaders, and calling the CPLEX solver to solve the lower level problem, optimizing its equipment output, demand response, and purchase and sale electricity plans. Finally, the proposed model was validated through numerical examples to effectively improve the economic and low-carbon performance of building integrated energy systems.