This paper presents our solution for the Micro-Expression Visual Question Answering (ME-VQA) task in the 2025 Facial Micro-Expression Grand Challenge (MEGC). To address the limitations of traditional micro-expression recognition (MER) methods in dynamic modeling, semantic interpretation, and natural language interaction, we propose Emotion-Qwen-VL, a fully fine-tuned multimodal large language model tailored for micro-expression understanding. Specifically, we construct a structured, instruction-based QA dataset that reformulates emotion categories and action unit (AU) annotations into natural language QA pairs, covering classification, AU detection, and causal reasoning. We then adopt a full-parameter fine-tuning strategy to guide Qwen2.5-VL in learning fine-grained temporal facial dynamics and their emotional semantics. Experimental results on the MEGC 2025 test set demonstrate that Emotion-Qwen-VL outperforms strong baselines such as Qwen2.5-VL and QVQ across multiple dimensions, including coarse-grained macro-expression classification, fine-grained micro-expression classification, and language generation. Our results highlight the effectiveness, interpretability, and adaptation potential of large models in micro-expression understanding. The code is available at: https://github.com/2308623956/MEGC2025.