This paper presents the system developed for the EvaHan 2026 shared task on Ancient Chinese OCR and Layout Analysis. Participating in the Closed Track, we propose a highly parameter-efficient, data-centric framework based on the Qwen2.5-VL-7B-Instruct multimodal large language model (MLLM). While the official baseline utilizes the same backbone architecture, our approach significantly outperforms it by integrating orientation-aware image preprocessing and expert-constrained adaptive prompt engineering. We employed Low-Rank Adaptation (LoRA) with a minimal rank configuration (Rank=16) to train three independent, task-specific adapters. Our system achieved exceptional results, recording an Overall score of 0.9703 and an F1-score of 97.19% on printed text recognition (Task A)—effectively halving the baseline’s Character Error Rate. On handwritten texts (Task C), we maintained a highly competitive 90.18% F1-score. Furthermore, our model achieved significant progress in layout analysis (Task B), surpassing the baseline’s Macro F1 by 172% (0.4162 vs. 0.1530) and mAP by 37%. These results underscore that embedding explicit document structure and semantic constraints into MLLMs is more effective than simply scaling model parameters.