Recent advancements in digital humanities have intensified the demand for intelligent processing of ancient Chinese texts, particularly across specialized domains such as historical records and ancient medical literature. Among related research areas, Named Entity Recognition (NER) plays a crucial role, serving as the foundation for knowledge graph construction and deeper humanities computing studies. In this paper, we introduce a architecture specifically designed for multi-domain ancient Chinese NER tasks based on a pre-trained language model (PLM). Building upon the GujiRoberta backbone, we propose the GujiRoberta-BiLSTM-Attention-CRF model. Experimental results on three distinct domain-specific datasets demonstrate that our approach significantly outperforms the official baselines across all three datasets, highlighting the particular effectiveness of integrating an attention mechanism within our architecture.

The deployment of Large Language Models (LLMs) faces significant challenges due to high computational costs,driving the demand for effective pruning techniques. Existing structured pruning methods employ uniform compression rates across network layers, neglecting the varying importance of different network depths. To address this limitation, we propose a novel optimization framework that directly minimizes global capability loss through layer-adaptive pruning rates. The framework formulates the pruning task as a combinatorial optimization problem constrained by a total parameter budget, and an efficient dynamic programming solution is derived to determine optimal layer-wise compression rates.Experiments demonstrate that, when tuning is not included, our approach achieves comparable performance with state-of-the-art methods at high pruning rates (37-50% reduction), and shows significant advantages at low pruning rates (13-25% reduction). When tuning is included, our method achieves the best performance among the compared methods.