Intelligent traffic light control can effectively improve the order and efficiency of road traffic. In urban traffic networks, special vehicles with urgent tasks have higher requirements for traffic efficiency. However, current intelligent traffic light control algorithms generally treat all vehicles equally, without considering the priority of special vehicles, while the traditional methods basically adopt signal preemption to ensure the priority of special vehicles, which has a great influence on the passage of ordinary vehicles. Therefore, this study proposes a traffic light optimization control method orient priority vehicle awareness. It learns traffic light control strategies through continuous interaction with the road environment. the weight of special vehicles is increased in state definition and reward function, and Double DQN and Dueling DQN are used to improve the performance of the model. Finally, the experiments are carried out in the urban traffic simulator SUMO. After the training stabilizes, compared with the fixed time control method, the proposed method can reduce the average waiting time of special vehicles and ordinary vehicles by about 68% and 22%, respectively. Compared with the method without considering priority, the average waiting time of special vehicles is also optimized by about 35%, all these results prove that the proposed method can not only improve the efficiency of all vehicles, but also give special vehicles higher priority. At the same time, the experiment also shows that the proposed method can be extended to apply in multi-intersection scenes.