Aiming at the problem of unbalanced quality of experience (QoE) caused by limited storage space of roadside unit (RSU) in edge caching, a vehicle user (VU) activity-aware cloud-edge collaborative data caching method is proposed to improve the fairness of content caching services and ensure QoE. The three-tier cloud-edge collaborative caching system model of the Internet of Vehicles is constructed to maximize the net revenue of the system and ensure the timeliness and effectiveness of content delivery by jointly optimizing service mode and cache replacement. VU is clustered based on different activity to obtain personalized content prediction results. The content request prediction model is designed, and the global model update is realized by using the federated learning framework. The problem of maximizing the long-term profit of the system is transformed into a multi-agent markov decision process (MDP), and the multi-agent deep deterministic policy gradient (MADDPG) algorithm is used to obtain the optimal decision. The simulation results verify the effectiveness of the solution. Compared with other baseline methods, the average system revenue and cache hit rate are improved, and the system fairness is effectively ensured.