In order to study the performance of the particle swarm optimization (PSO) algorithm in optimizing the mixing degree of forest stands, this study constructs an optimization model for the mixing degree of natural secondary forest stands based on PSO. The secondary mixed forest in Hupingshan Nature Reserve, Hunan Province, was used as a case study to explore the optimization effect under different cutting intensities (5%, 10%, 15%). The results showed that the mixing degree and fitness of forest stands increased nonlinearly with the increase of cutting intensity, and the uniformity of mixing degree distribution was significantly improved. At a small scale, PSO reduces the running time by 98.8% (2.70 seconds vs. 239.67 seconds) compared to the mixed integer programming (MIP) method, with an optimal solution achievement rate of 70% and no significant difference in solution quality between PSO and MIP. In medium to large-scale scenarios, the convergence time of PSO is 41.5%-50.9% shorter than that of the genetic algorithm (GA) and artificial bee colony (ABC) algorithm, and the number of iterations is reduced by 21.3%. This confirms that PSO can achieve both optimization accuracy and efficient computational performance in solving forest mixing degree optimization problems.