Abstract: The implementation of peak-valley time-of-use（TOU） price strategy can effectively reduce the peak-valley difference of load and save investment for power grid, but the load characteristics in different seasons are quite different, which affects the formulation of the optimal peak-valley TOU price strategy. Therefore, this paper mainly studies the peak-valley TOU price pricing strategy and period partitioning model considering multiple seasonal characteristics. Firstly, the demand response architecture proposed in this paper is described in combination with the main innovations of this paper. Secondly, the k-means method is adopted to obtain the load curve of typical days in each season, and the improved moving boundary technology is adopted to partition the load curve of typical days in each season. The optimization model for peak-valley period partitioning is established by setting the period partitioning constraint factors and adopting the Davies-Bouldin index（DBI） as the objective function. Then, the price elasticity of demand considering seasonal characteristics and the pea-valley TOU price optimization model considering multiple seasonal characteristics are established, and the particle swarm optimization（PSO） algorithm is used to solve the model. RTS is used to verify and analyze the algorithm and model, which verifies the effectiveness and correctness of the method and model proposed in this paper.