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The intelligent control of traffic lights is a hot issue in the research of intelligent traffic. In order to adapt to dynamic traffic in a more timely and effective manner and further improve traffic flow efficiency at street intersections, a road indicator light control method based on Deep Q Networks was proposed. This method is based on the description of the road indicator control problem, constructs the reinforcement learning model of the road indicator control with the three elements of state, action and reward, and proposes the road indicator control method flow based on Deep Q Networks. To test the effectiveness of the method, the traffic data at the intersection of Shifu Avenue and Donghuan Avenue in Taizhou City, Zhejiang Province were compared and simulated in SUMO. The experimental results show that the traffic light control method based on Deep Q Networks has higher efficiency and autonomy in the control and scheduling of traffic indications, is more conducive to improving the throughput of intersections traffic flow, and has better performance in optimizing the stay delay, queue length and waiting time of intersections traffic flow.