In recent years, ride-sharing has gained popularity as a daily means of transportation. The primary challenge for large-scale online ride-sharing platforms is to design an efficient fleet management policy that reallocates vehicles to appropriate regions to receive orders, thereby improving the platform's cumulative revenue and order response rate. Combinatorial optimization algorithms and reinforcement learning methods are commonly employed for this task, but they typically learn a unified repositioning policy for all regions. However, different regions, such as hot and cold zones, may require different repositioning policies due to varying travel patterns. In this paper, we propose a multi-agent mixed hierarchical reinforcement learning approach, called MIX-H, for efficient large-scale fleet management by formulating it as a Markov decision process. MIX-H adopts multi-level controllers, including a leader controller and follower controller, for multi-level action learning. The leader controller plans the goal to be executed by the follower controller. Additionally, to improve the algorithm's stability, we introduce a MIX module to compute the total value of joint action. Finally, experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.