###### Abstract
Additionally, the global pointer (GP) model, which leverages relative positions through a multiplicative attention mechanism, offers a global perspective on the start and end positions for predicting entities and has demonstrated outstanding performance across various nested NER tasks [30; 31; 32; 33]. Despite these advances, current NER models still rely heavily on large training datasets, making pre-trained models crucial for embedding layers in NER tasks [34; 35]. Models such as BERT, MCBERT, RoBERTa, XLNet, and ERNIE, trained on extensive corpora, have significantly improved entity recognition accuracy [36; 37; 38; 39]. However, challenges remain in accurately recognizing rare entities and domain-specific terms. To address these issues, researchers have sought to enhance pre-trained models with lexicon information. Techniques like Softword, ExSoftword, and SoftLexicon incorporate lexicons to improve the recognition of domain-specific named entities [40]. For instance, [PERSON] et al. [41] introduced the BERT-IDCNN-CRF model, which integrates the SoftLexicon method, demonstrating impressive efficiency across multiple datasets. Similarly, [PERSON] et al. [42] improved recognition by incorporating lexicons and similar words into character representations. [PERSON] et al. [43] further enhanced the model capabilities by dynamically updating custom lexicon segmentation methods, thereby improving the identification of domain-specific terms and new entities. Additionally, incorporating syntactic information has been shown to significantly enhance NER performance [44]. For example, [PERSON] et al. [45] developed the BioKMRER model, which employed a key-value memory network (KVMN) to integrate syntactic information, achieving excellent results on biomedical datasets. [PERSON] et al. [46] demonstrated that adding context through additional sentences in BERT input systematically improved the NER performance. These methods, whether introducing lexicon information, syntactic data, sentence context, or even stroke information of Chinese characters [47], are aimed at improving the NER model performance in specialized domains. In summary, NER continues to face significant challenges in the field of biological disease research, particularly in accurately recognizing nested entities and domain-specific terms. These challenges are especially pronounced in the study of chicken diseases, where the acquisition of relevant corpora is difficult and the labor cost of data annotation is high. To address these issues, we collected and organized a chicken disease corpus comprising 20 million characters, trained a specialized word vector tailored to chicken disease terminology, and annotated a portion of these data with high precision. Building on this foundation, we developed a nested NER model, MFGFF-BiLSTM-EGP, which leverages multiple fine-grained feature fusion (MFGFF) and the efficient global pointer (EGP). The primary contributions of this paper are as follows:  
1. We constructed the MFGFF-BiLSTM-EGP model, which connects the fusion output of multi-fine-grained features to the BiLSTM neural network layer, and finally through a fully connected layer into the EGP to predict the entity position. In the MFGFF module we designed, the character encoder obtains character features by fine-tuning the RoBERTa pre-trained model, the word encoder acquires word features through word-character matching, word frequency weighting, and multi-head attention mechanism, and the sentence features are output using SBERT. MFGFF effectively integrates multiple fine-grained features. In addition, the introduction of EGP enables the prediction of nested entities by means of positional coding. 3. We constructed a comprehensive knowledge base for chicken diseases that included a 20-million-character corpus, a vocabulary containing 6760 specialized terms, a 200-dimensional word vector in the field of chicken diseases, and a high-quality annotated dataset CDNER that was curated under the guidance of veterinarians. The remainder of this paper is organized as follows.