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This paper proposes a novel large language model (LLM)-based approach for visual target navigation in unmanned aerial systems (UAS). By leveraging the exceptional language comprehension capabilities and extensive prior knowledge of LLM, our method significantly enhances unmanned aerial vehicles (UAVs) in interpreting natural language instructions and conducting autonomous exploration in unknown environments. To equip the UAV with planning capabilities, this study interacts with LLM and designs specialized prompt templates, thereby developing the intelligent planner module for the UAV. First, the intelligent planner derives the optimal location search sequence in unknown environments through probabilistic inference.Second, visual observation results are fused with prior probabilities and scene relevance metrics generated by LLM to dynamically generate detailed sub-goal waypoints. Finally, the UAV executes progressive target search via path planning algorithms until the target is successfully localized. Both simulation and physical flight experiments validate that this method exhibits excellent performance in addressing UAV visual navigation challenges, and demonstrates significant advantages in terms of search efficiency and success rate.