In view of the increasing demand for user experience quality， the difficulty in obtaining link status caused by highly mobile vehicles， and the time-varying problem of heterogeneous edge nodes providing resources to vehicles in Vehicle Edge Computing （VEC）， a VEC scheme based on Joint Task Offloading and Resource Optimization （JTO-RO） was developed. Firstly， without loss of the generality， a Vehicle-to-Infrastructure （V2I） transmission model was proposed by considering the intra-edge and inter-edge interference comprehensively. In the model， by introducing Non-Orthogonal Multiple Access （NOMA） technology， edge nodes did not rely on link status information and improved the channel capacity at the same time. Secondly， in order to enhance performance and efficiency of the system， a Multi-Agent Twin Delayed Deep Deterministic policy gradient （MATD3） algorithm was designed to formulate task offloading strategies， which were able to be adjusted dynamically through interactive learning with the environment. Thirdly， the synergies of the two strategies were considered jointly， and an optimization scheme was formulated with the goal of maximizing the task service ratio to meet the increasing user experience quality requirements. Finally， simulation was carried out on a real vehicle trajectory dataset. The results show that compared with three current representative schemes （the schemes using Random Offloading （RO） algorithm， D4PG （Distributed Distributional Deep Deterministic Policy Gradient） algorithm， and MADDPG （Multi-Agent Deep Deterministic Policy Gradient） algorithm as task offloading algorithms as task offloading algorithm， respectively）， the proposed scheme has the average service ratio improved by more than 20%， 10%， and 29%， respectively， in three scenarios （normal scenario， task-intensive scenario and delay-sensitive scenario）， verifying the advantages and effectiveness of the scheme.