## 1 Introduction
Generative LLMs like ChatGPT [11; 12] have demonstrated outstanding performance in tasks such as dialogue, text understanding, and reasoning. Extensive studies have shown that ChatGPT outperforms zero-shot learning models in most NLP tasks and even surpasses fine-tuned models in certain tasks. Furthermore, it has demonstrated remarkable capabilities in low-resource NLP tasks, marking a transition in NLP tasks from traditional PLMs to LLMs. Although information extraction methods based on prompt learning and LLMs have achieved remarkable results in various domains, their application to NER in shearer maintenance contexts faces two critical challenges. The first challenge lies in enabling LLMs to effectively process domain-specific technical terminology and complex logical relationships inherent in shearer maintenance documentation while mitigating potential comprehension errors and reasoning biases. The second challenge concerns designing specialized NER prompt templates that synergistically integrate domain knowledge with the linguistic pro  
Figure 1: Comparison of the proposed and previous models. cessing capabilities of LLMs, particularly given the inherent limitations of generic prompt frameworks in handling contextually dense industrial corpora. To address the above issues, this paper proposes a multi-level clue-guided prompt learning (MCP) method for NER in the shearer maintenance domain, as illustrated in Figure 1d. This method inputs the information to be identified into the MCP model, where the prompt learning layer encapsulates the information and multiple sub-prompts into structured prompts according to a predefined format. The connection of the structured prompts and the information to be identified is then fed into the LLM layer, which processes it through multiple stacked decoder layers for deep representation learning. The answer layer uses a parser to map the output of the LLM layer into a structured label space, yielding the recognition results for named entities in the shearer maintenance domain. The model extracts and learns trigger words related to entity recognition from the prompts to obtain context-aware prompt tokens. This enables the model to make accurate predictions without relying on fine-tuning with labeled data, while also reducing hallucination phenomena in the LLMs. Overall, our significant contributions are as follows. 1. To address the gap between pretraining and fine-tuning tasks in existing NER methods for the shearer maintenance domain, as well as the high dependency on labeled data, this study introduces LLMs and prompt learning. In the shearer maintenance NER task, the proposed method, based on LLMs and prompt learning, demonstrates superior performance. 2. We design multi-level entity extraction sub-prompts that utilize contextual information to form effective prompt clues. Restriction rules are applied to prevent the model from making data extraction errors and exhibiting hallucination phenomena. Furthermore, ontological concepts specific to the shearer maintenance domain are introduced to inject domain knowledge into the prompts, enhancing the method's domain adaptability. 3. The proposed method is evaluated on a self-constructed shearer maintenance dataset and compared with mainstream baseline methods. Experimental results indicate that the proposed method outperforms the baselines in terms of precision, recall, and F1 scores, demonstrating the effectiveness of MCP. Additionally, ablation experiments, conducted by removing layer-by-layer prompt clues, further validate the role and effectiveness of each sub-prompt clue.