Multi-access Edge Computing (MEC) is one of the key technologies of the future 5G net-work.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios.Meanwhile,with the development of IOV (Internet of Vehicles) technology,various delay-sensitive and compute-intensive in-vehicle applications continue to appear.Compared with traditional Internet business,these computation tasks have higher processing prior-ity and lower delay requirements.In this paper,we design a 5G-based vehicle-aware Multi-access Edge Computing network (VAMECN) and propose a joint optimization problem of minimizing total system cost.In view of the problem,a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed,consid-ering the influences of multiple factors such as con-current multiple computation tasks,system comput-ing resources distribution,and network communica-tion bandwidth.And,the mixed integer nonlinear programming problem is described as a Markov De-cision Process.Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize comput-ing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.