In order to solve the problems in traffic signal control, such as unreasonable timing of signal lights and congestion at intersections, we propose an urban intelligent traffic control algorithm based on the asynchronous advantage actor-critic (A3C). This algorithm leverages asynchronous advantages to abstract and represent traffic state features, enabling accurate perception of traffic conditions through parallel multithreading. Drawing inspiration from reinforcement learning techniques, the algorithm iteratively optimizes its internal parameters to obtain the optimal solution for traffic signal control within the shortest possible timeframe. To assess the algorithm's effectiveness, we conducted simulated experiments using the traffic simulation software SUMO, comparing its performance with three other commonly used traffic signal control algorithms. The simulation results reveal that compared to the Q-learning algorithm, this algorithm reduces the average delay time of vehicles at intersections by 14.1%, decreases the average queue length by 13.1%, and lowers the average waiting time by 13.5%. This traffic signal control algorithm can effectively alleviate urban road congestion and improve the traffic efficiency of road intersections.