Argument Mining is crucial for uncovering the logical structure and reasoning process within texts, particularly in educational scenarios such as argumentative essay analysis. The NLPCC 2025 Shared Task on Argument Mining in Chinese argumentative essays introduces two subtasks: Argument Component Detection (ACD) and Argument Relation Identification (ARI). In this paper, we propose two corresponding methods—Track1 and Track2—to address these challenges. For Track1, we design a fine-grained data augmentation strategy by refining category labels with domain-specific and contextual information, enabling Large Language Models (LLMs) to generate diverse, high-quality samples. These are then used to fine-tune models through LoRA to improve category understanding. For Track2, we introduce a knowledge-enhanced relation identification method that combines prompt-based LLM generation with rule-based scoring and filtering to improve relation precision. In both tracks, we apply a majority voting strategy across multiple strong LLMs to enhance robustness. Experimental results demonstrate that our approach achieves state-of-the-art performance, validating the effectiveness of the proposed framework for fine-grained argument mining in Chinese essays.