The invention relates to a method based on 
edge computing and an Actor-Critic 
algorithm. The specific steps are as follows: in the vehicle networking communication 
system, the user sequence is {1, 2,..., k,..., K}, and there are K users; the subchannel sequence is {1, 2,..., n,..., N} with N subchannels; 
fog access node sequence {1, 2,..., m,... M}, total M access nodes, computing power sequence of incoming nodes {1, 2,... Cm,..., cM}; task sequence {1, 2,... tk,..., tK} uploaded by user, total tK tasks; a non-orthogonal multiple access (
NOMA) mode is adopted to connect the user to the vehiclenetworking communication 
system. The task uploaded by the user carries out edge calculation and returns the calculation result to the user; taking 
advantage of actor-Critic 
algorithm to optimize theresource 
allocation method and get the best 
resource allocation method. The invention combines the non-orthogonal multiple access, the edge calculation and the 
reinforcement learning, effectively solves the problem of huge access quantity existing in the vehicle networking, simultaneously reduces the time 
delay in the whole communication process, obtains the best 
resource distribution mode under different environments, and improves the energy utilization efficiency.