Kubernetes is a popular open-source container orchestration engine. Its default scheduling algorithm only considers CPU and memory and uses unified weight to calculate the score of candidate nodes, which cannot meet the requirements of different Pod applications. In view of this, the paper expands the Kubernetes performance indexes, with bandwidth, disk capacity, and IO rate added. The subjective weight is calculated by the analytic hierarchy process (AHP) and the objective weight of resource indexes is calculated by the entropy weight (EW) method in real time according to the resource utilization rate of performance indexes of nodes in the Pod application deployment process. We combine the two weights and apply them to a multi-attribute decision algorithm based on the improved technique for order preference by similarity to an ideal solution (TOPSIS) to select appropriate candidate nodes. The experiment results show that with the increase in the deployed Pod number, the standard deviation of the integrated load increases by 18% compared with that of the Kubernetes default scheduling algorithm under the condition of a large cluster load.