To address the issue of the lack of generalization capability of deep reinforcement learning in flexible job shop scheduling problems, a method combining curriculum learning and deep reinforcement learning was proposed. The training instance difficulty was dynamically adjusted, with an emphasis on enhancing the training of the most difficult instances, to adapt to different data distributions and avoid the forgetting problem during the learning process. Simulation test results demonstrate that the algorithm maintained decent performance on large-scale untrained problems and benchmark datasets. It achieves better performance on four large-scale untrained problems with two artificial distributions. Compared to exact methods and metaheuristic methods, for problem instances with larger computational complexity, it could rapidly obtain solutions of decent quality. Moreover, the algorithm can adapt to flexible job shop scheduling problems with different data distributions, exhibiting a relatively fast convergence speed and good generalization capability.