Abstract: The new generation of ground systems for space tracking, telemetry, and command (TT&C) and communication is composed of a large-scale heterogeneous cloud computing resource pool. To allocate available computing power to different TT&C and communication tasks reasonably, an intelligent scheduling method based on core allocation is proposed. This algorithm allocates different CPUs/GPUs for task processing based on the number of cores required by each task. This paper establishes a Markov decision process (MDP) model for the task core allocation problem, constructs a Q-table based on the number of tasks to be executed and the number of available processors for scheduling, formulates a principle of maximizing reward values for load balancing across processor resources, searches for the task allocation path that optimizes scheduling performance, iteratively calculates rewards and updates Q-values, completes multi-cycle training, and continuously iterates and optimizes the output scheduling results based on the reward circuit. Experimental analysis results show that this algorithm can maximize the utilization rate of computing cores while ensuring balanced task deployment. Compared with existing algorithms, it enhances the rationality of multi-core allocation and optimizes the overall system performance.