The design and realization of the AI scheduling engine platform based on Kubernetes is introduced in this paper. To tackle the problems of complex service configuration, the unbalanced utilization rate of computing resources of each node in the cluster and the high cost of system operation and maintenance in the current AI scheduling system, this study proposes a solution based on Kubernetes to implement container scheduling and service management. Combined with the requirements of the AI scheduling engine platform, the various modules of the platform are designed from such aspects as function implementation and platform architecture. At the same time, given the problem that Kubernetes cannot perceive GPU resources, Device Plugin is introduced to collect GPU information on each node in the cluster and report it to the scheduler. In addition, as priority algorithms in Kubernetes scheduling strategy only considers the resource utilization rate and balance degree of the node itself, disregarding the differences in the demand of different types of applications for node resources, priority algorithms based on Pearson correlation coefficient (PCC) is put forward. The scheduling of Pod is determined by calculating the complementary degree of container resources demand and node resource utilization rate, thus ensuring the resource balance of each node after the scheduling.