To enhance the optimization and control mechanisms of large-scale air-conditioning load clusters and augment their adjustable capacity, a multi-objective optimization strategy driven by dynamic electricity carbon factors (DECF) was formulated for aggregating large-scale air-conditioning loads. Initially, a model characterizing the regulation properties of air-conditioning loads was developed. Subsequently, a multi-objective day-ahead scheduling model for air-conditioning loads was constructed, targeting three key objectives: user comfort, electricity expenses, and carbon emission costs. The SGEA algorithm (Steady-state and Generational Evolutionary Algorithm) was used to solve it, and the best compromise solution was objectively selected through grey target decision (GTD); at the same time, the air-conditioning loads with different parameters were aggregated based on the K-means clustering method, and the control switch strategy under the cluster was obtained through the obtained cluster center parameters, the three objectives were recalculated, and the calculation time cost was simplified. Finally, the effectiveness of the strategy proposed in this paper was illustrated by an example.