In vehicular edge computing (VEC), the execution of offloading task needs not only the task data uploaded by the requesting vehicle, but also the additional data to support the task to be executed successfully, and how to efficiently cache and access these supporting data becomes the key issue for task offloading in VEC. In this paper, we study the efficient caching mechanism to minimize the acquisition delay of the supporting data. Firstly, with the software defined network (SDN) based VEC framework, we analyze the acquisition ways of the supporting data and the caching collaboration between VEC servers. Then, according to the density of the requesting vehicles, we divide the VEC coverage into dense and ordinary areas. With the consideration of the similarity of the requested data and the distance between edge servers, the edge servers are clustered into multiple groups based on K-mean++ algorithm. Finally, each server's storage space is divided into three partitions, and the most beneficial data for itself, its group and the whole system are respectively stored in these partitions. Based on service area dividing, server grouping and storage space partitioning, we propose an efficient edge-cloud collaborative caching strategy, which can reduce the delay of data migration while task execution. Simulation results show that, compared with other schemes, the proposed caching strategy has better performance in terms of average data migration delay and application QoS.