In recent years, mobile data has grown explosively due to the rapid development of Internet of Vehicles (IoV). However, resources of IoV are limited, in order to alleviate the problem of resource shortage, it is necessary to combine the resource rich aerial cloud and the ground edge nodes. In order to improve efficiency of proactive cache, we propose a proactive cache decision algorithm based on prior knowledge and aerial cloud assistance. Firstly, we divide requests into two types: content download requests and task calculation requests. Then the dynamic request graph based on relationship between users and requests is constructed, temporal graph network and long short term memory are used to predict prior information and caching benefit function is proposed based on popularity and supplemented by prior information to indicate cache location of request content. Finally, the problem of maximizing cache benefit is proposed and the theoretical solution is obtained using Lagrange multiplier method as well as simulation solution is obtained based on Deep Deterministic Policy Gradient. The simulation results demonstrate that the proposed caching scheme can greatly improve caching efficiency, reduce latency and energy consumption.Compared to D3QN, Dueling DQN, and Double DQN, system revenue of proposed algorithm has increased by 66.65%, 177.71% and 36.08%.