Autonomous driving vehicle decision-making system has direct influence on driving performance. It is one of the key challenges to be addressed to realize fully autonomous driving. To solve this problem, a driving decision-making system based on deep reinforcement learning algorithm deep deterministic policy gradient (DDPG) was proposed. Firstly, a total of 64 dimensions of state spaces information such as ego vehicle information, road information and obstacle vehicle information on the basis of a driver model were selected as input variables of the constructed model. Then the decision-making was trained and outputs reasonable driving behaviors and control variable values. Finally, aiming at the problems of reward value and control variable values saltation, the DDPG decision model was improved to optimize decision control effect. To verify the performance of the proposed decision making model, simulation experiments were conducted on the open racing car simulator (TORCS) platform. The results show that the proposed decision-making model can output reasonable driving behaviors and accurate control quantities based on real-time state information of vehicles and environment. Compared with the DDPG model, the improved decision-making model has better control accuracy, significantly reduces vehicle lateral speed, improves vehicle comfort and stability.