Space-air-ground integrated networks (SAGIN) have been envisioned as the promising and key network architecture for the 6G vehicular networks to provide seamless coverage for the connected vehicles. To access the most appropriate network quickly, this paper proposed a knowledge-driven network access approach, where the communication knowledge is explicitly integrated into neural networks, to deal with multiple tasks in SAGIN-based vehicular networks. Specifically, the formulated long-term network access problem is handled by asynchronous advantage actor-critic algorithm (A3C) in reinforcement learning. During this process, the space-time correlation knowledge is introduced to effectively reduce the action space in channel selection and the reward shaping exploiting the problem-specific communication and mathematical knowledge is adopted to solve the sparse reward problem in reinforcement learning. In addition, by modifying the sub-net learning rate of the A3C algorithm with experimental experience, this paper speeds up the network convergence speed by 1.5%. Numerical results also show that integrating knowledge into traditional deep reinforcement learning algorithm can improve the reward by 4%.