This paper proposes an AoI-aware resource allocation and RIS phase-shift control scheme for RIS-aided IoV networks using the Soft Actor-Critic (SAC) algorithm. The goal is to minimize the Age of Information (AoI) of V2I links and improve the payload transmission success rate of V2V links. The system model includes RIS-assisted V2I and V2V communication, with vehicles randomly distributed on a road network. The BS acts as an agent to allocate resources and control RIS phase-shifts. The problem is formulated as a Markov Decision Process (MDP), and the SAC algorithm is used to optimize the resource allocation and phase-shift control. The proposed scheme outperforms other algorithms such as PPO, DDPG, and TD3 in terms of convergence speed, cumulative reward, AoI performance, and payload transmission probability. Simulation results show that the SAC-based approach achieves better performance in minimizing AoI and improving V2V link reliability. The study highlights the effectiveness of deep reinforcement learning in addressing the challenges of high-dimensional state spaces and dynamic decision-making in vehicular networks. The results demonstrate that the proposed method enhances the efficiency and stability of RIS-assisted IoV communication.This paper proposes an AoI-aware resource allocation and RIS phase-shift control scheme for RIS-aided IoV networks using the Soft Actor-Critic (SAC) algorithm. The goal is to minimize the Age of Information (AoI) of V2I links and improve the payload transmission success rate of V2V links. The system model includes RIS-assisted V2I and V2V communication, with vehicles randomly distributed on a road network. The BS acts as an agent to allocate resources and control RIS phase-shifts. The problem is formulated as a Markov Decision Process (MDP), and the SAC algorithm is used to optimize the resource allocation and phase-shift control. The proposed scheme outperforms other algorithms such as PPO, DDPG, and TD3 in terms of convergence speed, cumulative reward, AoI performance, and payload transmission probability. Simulation results show that the SAC-based approach achieves better performance in minimizing AoI and improving V2V link reliability. The study highlights the effectiveness of deep reinforcement learning in addressing the challenges of high-dimensional state spaces and dynamic decision-making in vehicular networks. The results demonstrate that the proposed method enhances the efficiency and stability of RIS-assisted IoV communication.