"This paper introduces the CCL25-Eval evaluation task for ancient poetry comprehension and inference, which aims to enhance the capabilities of large language models(LLMs) in processing context-dependent texts with strong cultural backgrounds. Addressing the dual challenges of se-mantic analysis and emotional inference in ancient poetry, we propose a solution that integratesQwen-series LLMs with systematic data augmentation and LoRA-based parameter-efficient fine-tuning. We construct a high-quality dataset and design multi-phase training and inference strategies. Particularly in emotional inference tasks, we explore two approaches: emotion lexicon-based indirect matching and emotion appreciation-based direct judgment of emotional lexicon options. Experimental results indicated that: 1) Data augmentation significantly improves the model’s overall performance; 2) The result of emotion appreciation-based direct judgment approach achieves an accuracy of 0.865, ranking first in Task A; 3) Attempts with Qwen3 and reinforcement learning approaches do not significantly improve Task B results, but demonstrated good performance in sentence semantic similarity scores and format stability."