With the rapid development of Internet of Vehicles technology, edge computing plays an increasingly important role in information processing applications of Internet of Vehicles. However, the computing resources and storage capa-bilities of vehicle equipment in the Internet of Vehicles are limited, and vehicles need to process large amounts of data and complex computing tasks. How to efficiently utilize edge computing resources is an important issue. A single offloading method may not be able to meet the needs of the scenario. Consider the collaboration of multiple offload-ing methods to improve the overall performance and flexibility of the system. In response to the above challenges, a multi-mode collaborative vehicle network edge computing task offloading solution based on deep reinforcement learning is proposed. By using Vehicle to Vehicle (V2V), Vehicle to Vehicle Alliance (V2A), Vehicle to Roadside Unit (V2R) and Roadside Unit to Base (Roadside Unit to Base) Station, R2B) and other communication methods realize collaborative processing of tasks with high computing density and high latency requirements. In order to cope with the complexity of the dynamic network environment, the Markov Decision Process (MDP) is used for problem modeling and optimization, and deep reinforcement learning is introduced to handle continuous actions and state space. In particular, the MCTO algorithm is proposed, which can efficiently adapt to the dynamic environment of the Internet of Vehicles and significantly reduce the delay of the entire Internet of Vehicles system. The simulation results show that the proposed MCTO algorithm has good convergence and is significantly improved in terms of system delay compared with other reinforcement learning algorithms, with the overall performance improved by 28.67%.