###### Abstract  
Currently, few-shot setting and entity nesting are two major challenges in named-entity recognition (NER). Compared to English, Chinese NER not only has issues such as complex grammatical structures, polysemy, and entity nesting but also faces low-resource scenarios in specific domains due to difficulties in sample annotation. To address these two issues, we propose a Chinese few-shot named-entity recognition model that integrates multi-label prompts and boundary information (MPBCNER). This model is an improvement based on a pre-trained language model (PLM) combined with a pointer network. First, the model uses multiple entity label words and position slots as prompt information in the entity recognition training task. Activating the relevant parameters in PLM associated with the corresponding entity labels through the prompt information improved the model's performance in entity recognition under small-sample data. Secondly, by using a Graph Attention Network (GAT) to construct the boundary information extraction module, we integrated boundary information with text features, allowing the model to pay more attention to features near the boundaries when recognizing entities, thereby improving the accuracy  
have introduced CNN units based on the BiLSTM-CRF model to extract morphological information from word characters, thereby improving the model's final performance. In recent years, with the successive emergence and development of large-scale pre-trained models (e.g., Bert Bert (2016), GPT (2016); Gret (2016); Gret and Gret (2017), RoBERTa (2018), T5 (2018), ERNIE (2018, 2019, 2020), etc. ), the large-scale training parameters have enabled these large models to possess better knowledge representation and transfer learning capabilities, thereby achieving the latest and optimal results in many natural language processing tasks. [PERSON] et al. [PERSON] et al. (2019) proposed a BERT-CRF model for the NER task, combining BERT's transfer capabilities with CRF's structured prediction, achieving results better than those of the aforementioned baseline methods. However, both traditional sequence labeling methods and fine-tuning modes of downstream tasks based on large models require a large amount of sample data during the training phase. In practical applications, obtaining large-scale labeled data may face difficulties, requiring industry experts to spend a lot of time and effort to complete it manually [PERSON] et al. (2019). In small-sample scenarios, due to the limited labeled data, it is difficult for the model to learn the complex features and constraints of different entity types, leading to unsatisfactory entity-recognition results. To harness the potential of large models in small-sample-data scenarios, researchers have adopted a range of methods, including transfer learning [PERSON] et al. (2018), contrastive learning [PERSON] et al. (2018), knowledge graph injection [PERSON] and [PERSON] (2017), and prompt learning [PERSON] (2017). These methods have been used to enhance a model's generalization ability, thereby improving its performance on small samples. Among these, prompt learning, as an emerging model adaptation paradigm, has emerged as a particularly efficient and flexible solution for model applications in small-sample-data scenarios through the embedding mechanism of natural language prompts, garnering widespread attention from researchers in the field. [PERSON] et al. [PERSON] et al. (2019) proposed TemplateNER, which was the first to apply the prompt method to NER, predicting entity types by manually constructing discrete templates and enumerating all possible entity spans. The model has been shown to significantly outperform traditional sequence labeling methods in cross-domain and few-shot setting. However, the prediction results are heavily dependent on the quality of the template design, and the introduction of a large number of negative samples leads to severe efficiency issues. [PERSON] et al. [PERSON] et al.