The rapid development of artificial intelligence has led to a continuous expansion in the scale of the neural network models being trained, resulting in the gradual insufficiency of model training resources to support the increasingly larger models. Electric vehicles have become more intelligent thanks to their increasingly powerful computing resources. When these vehicles are not in operation, a significant amount of computing resources are left idle. This paper proposes a new distributed machine-learning system, i.e., virtual data center with parking electric vehicles (PDC), which is composed of central servers, hybrid local area networks, and electric vehicles in parking lots. The PDC is aimed at utilizing the powerful computational capabilities of electric vehicles in parking lots to provide robust computational resources for model training in favor of organizations such as universities and companies. This is accomplished by collecting jobs that need to be trained from the server, gathering information about the computation and communication resources of parking lots, and distributing the jobs that need to be trained to various electric vehicles. We further propose a reinforcement learning-based scheduling algorithm to minimize the job completion time for the jobs. The simulation results justify the feasibility of the PDC system and the efficiency of our proposed scheduling algorithm.