In artificial breeding of forest musk deer (Moschus berezovskii), common diseases such as abscess, enteritis, pneumonia, and parasitic infections exhibit persistently high morbidity rates. The early symptoms of certain diseases are often insidious and difficult to discern. Conventional manual inspection routines not only fail to achieve accurate diagnosis but also frequently disturb the animals, induce stress responses, and consequently delay optimal treatment windows. To address this practical challenge, this study employs an improved BRW-GPLinker joint entity-relationship extraction approach to perform integrated extraction and structural organization of disease entities, symptom manifestations, etiological associations, and preventive and therapeutic measures from farming literature and clinical records, thereby constructing a disease knowledge graph for forest musk deer. Through the introduction of a Boundary-Aware Module for refined entity boundary detection, a Relative Distance Bias Module to mitigate pairing errors in dense contexts, and a Weighted Sparse Multi-label Cross-Entropy loss function to enhance recall for infrequent relations, the proposed model achieves an F1 score of 0.887 on a self-constructed dataset and demonstrates favorable generalization capability on medical-domain datasets. By transforming fragmented clinical logs and manuals into structured medical associations, this knowledge graph facilitates rapid retrieval of forest musk deer disease information, thereby enhancing veterinary decision-making efficiency and assisting forest musk deer health management.

The expansion of captive breeding of the forest musk deer (Moschus berezovskii)—a first-class protected species in China with significant economic and medicinal value—has led to escalating disease threats, underscoring the need for intelligent disease management based on knowledge graphs. However, the unstructured nature of veterinary texts, characterized by nested entities, ambiguous boundaries, and sparse relations, poses substantial challenges to accurate joint entity-relation extraction. To address these issues, this study proposes BRW-GPLinker, an enhanced joint extraction model built upon the GPLinker framework. This model integrates a Boundary-Aware Module (BAM) for precise entity boundary detection, a Relative Distance Bias Module (RDBM) to minimize pairing errors in dense contexts, and a Weighted Sparse Multi-label Cross-Entropy (WSMCE) loss function to improve recall for infrequent relations. Experimental results on the constructed MS-Data dataset demonstrate that BRW-GPLinker achieves an F1 score of 0.887, outperforming the baseline GPLinker by 2.0 percentage points. It also exhibits strong generalization, with an F1 score of 0.590 on the general-domain CMeIE-V2 dataset. These findings confirm that the proposed model provides reliable support for constructing disease knowledge graphs in forest musk deer farming.