Abstract: Vehicle Edge Computing (VEC), combining mobile edge computing (MEC) with the Internet of Vehicles (IoV) technology, offloads vehicle tasks to the edge of the network to solve the problem of limited computing power at the vehicle terminal. In order to overcome the difficulty of on-board task scheduling due to the sudden increase in the number of tasks and provide a low-latency service environment, the vehicle tasks are divided into three types of main tasks by using improved Analytic Hierarchy Process (AHP) according to the dynamic correlation change criteria of the selected five feature parameters, and the joint modeling of resource allocation is carried out based on three kinds of offloading decisions. Then, the constraints of the modeling are eliminated by using scheduling algorithm and penalty function, and the obtained substitution value is taken as the input for the following deep learning algorithm. Finally, a distributed offloading network based on deep learning is proposed to effectively reduce the energy consumption and delay of VEC system. The simulation results show that the proposed offloading scheme is more stable than traditional deep learning offloading scheme and has better environmental adaptability with its less average task processing delay and energy consumption.