In recent years, deep reinforcement learning has been widely used in sequential decisions with positive effects, and it has outstanding advantages in application scenarios with high-dimensional input and large state spaces. However, deep reinforcement learning faces some limitations such as a lack of interpretability, inefficient initial training, and a cold start. To address these issues, this study proposes a dynamic decision framework combing explicit knowledge reasoning with deep reinforcement learning. The framework successfully embeds the priori knowledge in intelligent agent training via explicit knowledge representation and gets the agent intervened by the knowledge reasoning results during the reinforcement learning, so as to improve the training efficiency and the model’s interpretability. The explicit knowledge in this study is categorized into two kinds, namely, heuristic acceleration knowledge and evasive safety knowledge. The heuristic acceleration knowledge intervenes in the decision of the agent in the initial training to speed up the training, while the evasive safety knowledge keeps the agent from making catastrophic decisions to keep the training process stable. The experimental results show that the proposed framework significantly improves the training efficiency and the model’s interpretability under different application scenarios and reinforcement learning algorithms.