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In order to improve the vehicle cruise obstacle avoidance ability and achieve accurate decision-making on moving targets, a vehicle multi-target collaborative cruise decision-making control system based on deep reinforcement learning is designed. The electricity signal output from the main control circuit is used to adjust the real-time connection state of ACC controller, MPC track tracker and double closed loop controller. Then, with the help of multi-objective decoupling module, the cruise position of the target vehicle is determined, and the main application structure design of the cruise decision-making control system is completed. The depth reinforcement learning model is established. According to the definition conditions of vehicle target data set, the actual value range of collaboration parameters is solved to realize the estimation of vehicle cruise pose. Determine the coordinate conversion principle, realize the on-demand planning of the cruise decision-making trajectory by analyzing the multi-target quantitative results, and then combine relevant application equipment to complete the design of the vehicle multi-target collaborative cruise decision-making control system based on in-depth reinforcement learning. The experimental results show that under the deep reinforcement learning mechanism, the obstacle avoidance accuracy of the vehicle in both horizontal and vertical cruising directions reaches 100%, which meets the actual requirements of vehicle multi-target cooperative cruise decision-making.