In order to solve the problem that existing hierarchical navigation methods based on deep reinforcement learning (DRL) perform poorly in complex environments contain structures like long corridors and dead corners, in this study, we propose a navigation method for mobile robot based on option-based hierarchical deep reinforcement learning (HDRL). The framework of the proposed method consists of two low-level control models to obtain policies for avoiding obstacles and reaching the goal respectively and a high-level behavior selection model for automatically learning stable and reliable behavior selection policy, which does not rely on manually designed control rules. In addition, a training method for optimizing the obstacle avoidance control model is proposed, which make the learned obstacle avoidance policy more suitable for the navigation task in complex environments. In comparison with existing DRL-based navigation methods, the proposed method achieves the highest navigation success rate in all simulated test environments used in this paper and shows better overall performance on other metrics, which demonstrates the proposed method can effectively solve the problem of poor navigation performance in complex environments and has strong generalization ability. Moreover, experiments in real-world environment also verify the potential application value of the proposed method.