In response to the challenges of resource constraints and high load in road side unit (RSU) within vehicular edge computing (VEC), as well as the limitations of existing task offloading optimization schemes that focus solely on reducing latency or energy consumption while neglecting the security issues faced by edge nodes, this study proposes a task offloading scheme based on trust awareness and the proximal policy optimization (PPO) algorithm. First, a VEC network architecture is constructed, which utilizes the computing resources of nearby idle vehicles to process tasks locally or offload them to RSU or idle service vehicles, in order to reduce the overall system latency and energy consumption. Second, a dynamic feedback trust evaluation model based on multi-source weighting and a reward-punishment mechanism is constructed to achieve a quantitative assessment of the trustworthiness of edge nodes. Finally, the task offloading strategy is optimized using the PPO algorithm based on deep reinforcement learning. Experimental results show that compared to the DQN, D3QN, and TASACO algorithms, the proposed scheme has better convergence and stability, and it outperforms existing schemes in terms of task execution latency and energy consumption.