To deal with the problem of dynamic scheduling of flexible job shop, a dynamic scheduling optimization model was constructed to minimize average delay penalty, energy consumption and deviation, and an ameliorated quantum genetic algorithm based on deep Q-learning neural network was proposed. First, a learning environment based on dynamic event disturbance and periodic rescheduling was built, and an environment-behavior evaluation neural network model was established using deep Q-learning neural network algorithm as the fitness function of the optimization model. Then the dynamic scheduling optimization model was solved by using the improved quantum genetic algorithm which designed a multi-layer encoding and decoding scheme based on process encoding and equipment encoding. A strategy for dynamically adjusting the rotation angle based on fitness was developed to improve the convergence speed of the population and exclude local solutions by combining with chaos-based Tent mapping search. Finally, test cases verified the robustness and adaptability of the environment-behavior evaluation neural network model, as well as the effectiveness of the optimization algorithm.