To address the problems of unstable training, slow convergence and frequent phase changes of signal control methods based on deep reinforcement learning, a signal control method that integrates domain expertise was proposed by incorporating a pre-training module and a phase green time calculation module based on the double-dueling deep Q network (3DQN) algorithm. The pre-training module was introduced to guide the 3DQN agent to mimic the strategy of Max-Pressure method by optimizing the dual Q learning loss, supervised marginal classification loss and regularization loss, whereby the training process was stabilized and accelerated. The phase green light time calculation module dynamically adjusted the phase green light time to reduce green light loss based on the average time headway and queue length of the current phase. The intersection of Airport City Avenue and Boao Road in Xiaoshan District, Hangzhou was used as an example to verify the algorithm on the simulation platform SUMO. The simulation test results show that the proposed method can not only effectively improve the training speed of the traditional 3DQN algorithm, but also significantly reduce the average vehicle travel time and improve the intersection operation efficiency compared with the traditional control method.