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With the development of the Internet industry, shortcomings such as slow creation of virtual machines, difficult expansion, and insufficient flexibility have become more and more prominent. The emergence of container technology has proposed a new solution to these problems. The existing scheduling algorithm only considers the memory, CPU and other physical resources of the working nodes in the container cloud cluster, and does not consider the network load rate that has a significant impact on the image distribution process after the container cloud scheduling, resulting in too long waiting time for container scheduling tasks. Cause a waste of resources in the data center. Considering that the particle swarm optimization algorithm has strong advantages in local mining capabilities and global detection, a simulated annealing algorithm-based particle swarm optimization algorithm (Simulated annealing particle swarm optimization algorithm, SA-PSO) container scheduling algorithm is proposed. Using simulated annealing to optimize the particle swarm algorithm makes it jump out of the local optimal situation in the early stage of the algorithm, and improves the performance of the algorithm. In the process of the Kubernetes platform experiment, the SA-PSO scheduling algorithm compared to Kubernetes" BalancedQosPriority algorithm improves the overall node resource utilization and significantly reduces the minimum waiting time of tasks; at the same time, it is compared with the standard PSO algorithm and the dynamic inertia weight PSO algorithm, which not only converges The ability has been significantly improved, and compared with the standard PSO algorithm, the global optimal node hit rate has increased by nearly 60%.