## 1 Introduction  
As one of the key pieces of equipment in fully mechanized mining systems, the shearer's efficient and stable operation is critical for ensuring coal mine production efficiency, guaranteeing safe operations, and reducing maintenance costs. Various multi-source data, such as shearer maintenance manuals and professional books, serve as vital knowledge sources for extracting and summarizing maintenance concepts and practical experience. Accurately identifying and extracting diverse entity information related to shearer maintenance is an essential prerequisite for enabling intelligent operation and maintenance downstream tasks such as knowledge graphs, question-answering systems, and information retrieval. It plays an irreplaceable role in the intelligent framework of coal production processes. Therefore, there is an urgent need to develop efficient named entity recognition (NER) methods in the field of shearer maintenance. NER refers to the process of identifying entities with specific meanings in a text and categorizing them into predefined entity types. Early NER methods primarily relied on expert-designed rules and templates to identify named entities [1]. However, these approaches had limitations, including high rule-development costs and poor transferability to other corpora [2]. With the rise of machine learning in the field of natural language processing (NLP), researchers have explored NER using machine learning methods, such as hidden Markov models (HMMs) [3], conditional random fields (CRFs) [4], maximum entropy models (MEMs) [5], and support vector machines (SVMs) [6]. These methods rely on learning features and patterns of input text from large corpora and utilize feature engineering to extract morphological characteristics of the input text to achieve NER. However, machine learning methods often require manually designed features and exhibit limited generalization capability. With the continuous development of deep learning technology, deep learning-based methods typically leverage large-scale, high-quality annotated corpora to train complex network models, resulting in better entity recognition performance compared to machine learning methods. For example, various sequence labeling models based on LSTM [7], such as LSTM, Bi-LSTM, and Bi-LSTM-CRF, have been proposed. In recent years, with the emergence of pretrained language models (PLMs), the bidirectional encoder representations from transformer (BERT) model [8], based on transformer architecture, has significantly enhanced the understanding of syntactic and semantic information, bringing substantial improvements to various NLP tasks. The advent of pretrained models has driven a new research paradigm known as \"fine-tuning\". Fine-tuning refers to using PLMs as backbone models and performing targeted training on some or all of their parameters using labeled target datasets for specific downstream tasks. Researchers construct models by appending network structures specifically designed for downstream tasks to the end of PLMs. NER in the shearer maintenance domain primarily employs methods based on the fine-tuning paradigm. As shown in Figure 1a,b, during the fine-tuning process, the appended network models may introduce structural inconsistencies, and the differing objectives of upstream and downstream tasks further constrain their performance. Moreover, these methods heavily depend on the quality and size of labeled datasets. Recently, some studies have proposed a novel approach called prompt learning [9; 10]. This method bridges the gap between pretraining and downstream tasks by converting various downstream tasks into optimal cues or front-end prompts, such as cloze tests. As shown in Figure 1c, this is an example of a prompt learning method. This paradigm integrates the original input with a prompt template containing \"[MASK]\", then feeds the combined sequence into the language model to predict the \"[MASK]\".