Coordinating distributed intelligent agents with limited coverage angle and distance to maximize coverage rate is a challenging problem in target coverage control tasks. This paper proposes a hierarchical planning-based multi-target coverage control algorithm to accomplish cluster target coverage tasks under constrained capabilities. The multi-agent cluster target coverage problem is decomposed into two sub-tasks. The high-level planner is responsible for allocating task targets to each agent, achieving multi-task optimal matching through the hungarian algorithm. The low-level executor conducts target tracking according to the assignment results, constructing a reinforcement learning model to obtain the action sequence of individual agents. The effectiveness of the proposed algorithm framework is validated within a simulation environment. The experimental results indicate that the proposed method can realize the agent coordination in different scenarios, and has good generalization and scalability.