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Vehicular edge computing is a new computing paradigm. To make the service offloading efficiently under vehicular edge environment, by both considering the service offloading strategy and the collaborative allocation of edge server and cloud server, a Deep Q-network (DQN) based Service Offloading DQN (SODQN) algorithm was proposed. An End-Edge-Cloud architecture was proposed for service offloading and the problem of service offloading was formulated as the optimization problem under the constraints of the computing and communication resources of the edge server. DQN was used to solve the optimization problem, where greedy algorithm, experience replay mechanism and double DQN mechanism were introduced in the learning process. The extensive simulation experiments were conducted, and the experimental results showed that the proposed offloading scheme could achieve a good performance.