## 1 Introduction
[17] proposed a model, LEBERT, to solve the Chinese sequence labeling task, mainly through injecting lexical features into the BERT (Bidirectional Encoder Representation from Transformers) model [18] to enhance words. [PERSON] et al. [19] proposed a method for constructing a multimodal domain knowledge graph based on LEBERT to solve the problem of vast and dispersed knowledge systems in computer science. [PERSON] et al. [20] introduced an external dictionary to enhance BERT features and the adversarial training entity recognition model LEBERT-BCF, solving the problems of lack of word information, waste of entity boundary information, and poor model robustness in Chinese medical record entity recognition. Most of the above entity recognition research focuses on general domain entity recognition. Due to the presence of a large number of domain entities in the domain dataset, the above methods do not apply to the entity recognition task in the complex mechanical equipment domain without the assistance of domain knowledge. In recent years, several noteworthy studies have emerged, offering innovative approaches to enhancing low-resource NER. [PERSON] et al. [21] proposed a method using various adaptations of BERT to enhance the performance of low-resource NER. [PERSON] et al. [22] introduced a Translation-and-fusion framework, which translates a low-resource language text into a high-resource language for annotation using fully supervised models before fusing the annotations back into the low-resource language. [PERSON] et al. [23] proposed a two-stage learning pipeline to tackle oncological NER task in Chinese language, which is a typical task lacking training resources. [PERSON] et al. [24] proposed SeqUST, a novel uncertain-aware self-training framework for NSL to address the labeled data scarcity issue and to effectively utilize unlabeled data. [PERSON] et al. [25] proposed a CP-NER model to solve the problem of cross-domain resource scarcity in practical scenarios. Experiments show that the model has good performance on both single-source and multi-source cross-domain tasks. [PERSON] et al. [26] proposed a novel data augmentation framework, BioAug, for low-resource biomedicine. It is used to solve new text reconstruction tasks based on selective masking and knowledge enhancement. [PERSON] et al. [27] proposed Attention-map aware keyword selection for Conditional Language Model fine-tuning, a new data augmentation method based on conditional generation to address the data scarcity problem in low-resource complex NER. [PERSON] and [PERSON] [28] proposed a multilingual complex named entity recognition method using XLM-RoBERTa. This method utilizes a pre-trained language model for task transfer learning to achieve cross-language and cross-domain entity recognition. [PERSON] et al. [29] proposed a named entity recognition method called GPT- NER in 2023 using a large language model for training, which bridges the gap through transforming a sequence labeling task to a generation task that can be easily adapted by LLMs. In response to the problem of entity recognition for complex mechanical equipment under low-resource conditions, this study proposes a method for recognizing named entities in Chinese for complex mechanical equipment faults, incorporating dictionary information based on the LEBERT model proposed by [PERSON] et al. [17]. The difference between this method and that of [PERSON] et al. is that, in order to integrate domain knowledge better, this study uses the Word2 Vec [30,31] method in the open-source library Gensim [32] to train a domain vocabulary for mine hoist equipment and construct a domain dictionary for mine hoist equipment and faults. A dictionary adapter is embedded between the Transformer encoders in the BERT model to match characters with the domain dictionary and obtain word set information, which is integrated into the character representation to enhance lexical information. This addresses the issues of blurred entity boundaries and semantic loss in domain character representation.