Vehicle platooning is a key application in the realm of smart connected vehicles and autonomous driving technologies, holding significant potential to enhance road utilization and save energy consumption. Simultaneously, within intelligent transportation systems, the limited computing resources of vehicle users themselves fail to meet the computational demands of various new applications. If vehicle tasks cannot be processed promptly, it may lead to traffic safety incidents. Therefore, addressing the ever-increasing computational demands of vehicles is an urgent problem that needs resolution. Unmanned Aerial Vehicle (UAV) equipped with edge computing servers leverage their advantages of flexible deployment and high maneuverability to promptly alleviate issues such as high latency and narrow bandwidth associated with processing remote data in cloud computing. This paper focuses on the scenario of UAV-assisted vehicle platooning, conducting research on task offloading and resource allocation mechanisms within UAV-assisted vehicle platooning systems. Combining Lyapunov optimization theory and considering the coupling effects of vehicle platooning motion and vehicle communication, we construct a joint optimization problem for decision-making on task offloading, transmission power allocation, and CPU computing frequency allocation in UAVassisted vehicle platooning systems. The objective is to minimize system energy consumption while ensuring the stability of the task computation queue. Since the formulated joint optimization problem is a mixed-integer nonlinear programming problem, we initially decompose it into two sub-problems and simultaneously transform them into Markov decision processes. Subsequently, we proposed a continuous optimization algorithm based on Block Coordinate Descent (BCD) and deep deterministic policy gradient (DDPG). Simulation results validate the effectiveness of this method, demonstrating comparatively low energy consumption under different network environments and parameter settings.