###### Abstract
Lattice-LSTM [9] has achieved new benchmark results on several public Chinese NER datasets. However, the Lattice-LSTM model architecture is complex, which limits its application in many industrial areas requiring real-time NER responses. A convolutional neural network-based method that incorporates lexicons using a rethinking mechanism was proposed, which can model all the characters and potential words that match the sentence in parallel [10]. A lexicon-based graph neural network with global semantics was proposed to tackle word ambiguities. In this model, the lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency [11]. A Lexicon-Enhanced BERT (LEBERT) for Chinese sequence labeling was put forward [12]. The model integrates external lexicon knowledge into BERT layers directly with a Lexicon Adapter layer and achieves better performance than both lexicon-enhanced modelsand the BERT baseline in Chinese datasets. More character-word association models have been proposed, such as SoftLexicon [13], FLAT [14], and PLTE [15]. The pre-trained NER method effectively leverages deep bidirectional contextual information. It demonstrates superior performance with shorter training times, reduced labeling data requirements, and improved results compared to traditional models. Currently, BERT [16] is widely used, followed by ELMo [17], RoBERTA [18], ERNIE [19], ALBERT [20], and others. At present, the pre-trained models and lexicon are integrated by utilizing their respective strengths. [PERSON] proposed Flat-Lattice Transformer for Chinese NER, which converts the lattice structure into a flat structure consisting of spans [14]. [PERSON] proposed the LEBERT-BiLSTM-CRF model for elementary mathematics text NER, which integrates external lexicon knowledge into BERT layers directly with a lexicon adapter layer and performs better than other NER models [21]. Contrastive learning acquires feature representations of samples by comparing positive and negative samples in feature space. This approach has garnered significant attention in the fields of computer vision (CV) and natural language processing (NLP). The ConSERT (Contrastive Framework for Self-supervised Sentiment Representation Transfer) and SimCSE (Simple Contrastive Learning of Sentiment Embedding) models, which use different data enhancement methods and comparative learning loss function to learn the representation of sentences, obtain SOTA results on the task of text semantic similarity [22; 23]. Contrastive learning with prompt guiding for few-shot NER (COPNER) was proposed and outperforms state-of-the-art models with a significant margin in most cases. This method introduces category-specific words that COPNER composed out of prompts as supervised signals for contrastive learning to optimize entity token representation [24]. Moreover, Named Entity Recognition in low-resource scenarios based on contrastive learning has also received considerable attention [25; 26; 27]. He proposed a novel prompt-based contrastive learning method for few-shot NER without template construction and label word mappings [25]. [PERSON] proposed a multi-task learning framework CLINER for few-shot NER [26]. In the field of livestock husbandry, text mining, Named Entity Recognition (NER), intelligent question-and-answer systems, and artificial intelligence (AI) technologies have been gradually applied. However, this field faces numerous challenges, including the prevalence of technical terms, complex knowledge structures, fine knowledge granularity, and a lack of labeled datasets [28]. [PERSON] created a BERT-DIS-NER model that adds a CRF layer to BERT for disease named entity recognition and used syllable unit-based named entity recognition that can reflect the characteristics of disease names. The F1-score is 0.81, trained with human data and fine-tuned with animal data [29].