Argument mining in Chinese essays is a challenging task due to the diversity of argumentative structures. In NLPCC 2025 Shared Task 5, we explored the use of large language models for comprehensive argument analysis. For argumentative component detection, we decouple the task into two stages. We first perform coarse-grained classification, then refine the results with fine-grained classification, both accomplished through fine-tuning large language models. For argument relation identification, we apply automated prompt engineering, beginning with a manually designed seed prompt and iteratively expanding and testing candidate prompts on simple data to select and optimize the best template. Our approach enhances contextual understanding and achieves strong performance in both argumentative component detection and argument relation identification, with our method achieving first place in the evaluation.