## 1 Introduction  
Named Entity Recognition (NER) [1] is a tagging task aimed at identifying and categorizing named entities in a text according to predefined semantic types, including but not limited to individuals, organizations, and geographical locations. In the field of complex mechanical equipment, NER can be used to identify entities such as equipment, components, and types of faults. With NER technology, helpful information can be extracted more quickly and accurately from a large amount of text data, supporting fault diagnosis and repair. Given the specificity and industry-specific nature of the complex mechanical equipment field, it is necessary to develop specific NER models for this domain. Nonetheless, the availability of substantial amounts of labeled training data is vital for their triumph. Regrettably, the process of constructing extensive labeled datasets for every new domain of interest proves to be costly and time-consuming, necessitating a well-educated manpower for annotation tasks. Therefore, effectively addressing the problem of named entity recognition under low-resource [2] conditions has become an important research topic. While significant progress has been made in Named Entity Recognition (NER) tasks in resource-rich scenarios and low-resource scenarios, deep learning models struggle to train effective feature extraction capabilities due to the lack of annotated datasets. Therefore, enhancing model performance in low-resource NER tasks has recently become a research hotspot. Some researchers have adopted cross-lingual transfer methods [3], primarily transferring knowledge from resource-rich to low-resource languages. For instance, [PERSON] et al. [4] proposed a translation method based on bilingual word vectors to improve the mapping of cross-language text, replacing a Bidirectional Long Short-Term Memory (BiLSTM) [5] with a Self-Attention [6] mechanism as the encoder to enhance the robustness of different language word order differences. [PERSON] et al. [7] proposed a meta-learning algorithm to fine-tune a source language model using a few test examples of the target language to enhance the model's generalization ability across different languages. [PERSON] [8] et al. achieved an unsupervised cross-language NER model through word-level adversarial learning, parameter sharing, and feature-enhancing fine-tuning. In addition to cross-lingual transfer learning, some researchers have proposed cross-domain transfer [9], transferring knowledge from resource-rich to low-resource domains. [PERSON] et al. [10] proposed a domain NER model without external resources, enhancing zero-resource domain adaptability's robustness through multi-task learning and a hybrid entity expert framework. [PERSON] and [PERSON] [11] introduced an adaptive layer on top of an existing neural network structure, eliminating the need to retrain the source domain data. [PERSON] et al. [12] proposed a cross-disciplinary label-aware dual transfer system, mainly through two label-aware rules to achieve feature and parameter transfer. Unlike the two research directions above, some researchers have proposed cross-task transfer methods, attempting to improve NER task's effect through utilizing related information from other tasks, such as part-of-speech or boundary information. [PERSON] et al. [13] proposed a multi-classification model, co-training sentence-level labels, and word-level NER. [PERSON] et al. [14] used a model to co-train entity extraction and detection, relation extraction, and NER tasks. The above research revolves around transfer learning, but due to differences between different domains, it is challenging to ensure the effectiveness of knowledge transfer. Some researchers have adopted data augmentation [15; 16] methods to mitigate the impact of scarce annotated data to enhance NER performance. However, injecting lexical information into the model for lexical enhancement has become an effective way to improve Chinese entity recognition performance. [PERSON] et al.