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In order to improve the efficiency of intersection traffic and alleviate traffic congestion, a single intersection traffic signal control method based on Dueling Double DQN (D3QN) is proposed by deeply exploring the deep hidden feature information contained in traffic status information. A traffic signal control model based on deep reinforcement learning Double DQN (DDQN) was constructed, and the iterative operation process of the estimated value and target value of the action value function was optimized to overcome the problem of slow convergence speed in traffic signal control models based on deep reinforcement learning DQN. A new Dueling Network has been designed to decouple the value of traffic states and phase actions, enhancing the ability of Double DQN (DDQN) to extract deep level feature information. A single intersection simulation framework and environment were built based on the micro simulation platform simulation of urban mobility（SUMO）for simulation testing. The simulation test results show that compared with traditional traffic signal control methods and traffic signal control methods based on deep reinforcement learning DQN, the proposed method can effectively reduce the average waiting time, average queue length, and average number of stops of vehicles, significantly improving the efficiency of intersection traffic.