## 6 Conclusions  
This paper introduces SACT, a novel FSRE model. SACT incorporates a multi-head self-attention mechanism to capture inherent relationships among different category prototypes, addressing the limited generalization capabilities of traditional prototype networks when faced with new relationships. In addition, SACT employs an adaptive prototype fusion technique that combines relational information with enhanced prototypes, enhancing the overall performance of the prototype network. Moreover, SACT introduces a novel loss function, the contrastive-center loss. It effectively tightens the feature vectors of samples from the same class by maximizing the angle between negative pairs. This enhances sample distribution, improving intra-class closeness and inter-class distinguishability. This innovative loss function boosts the learning capacity of our model, offering a fresh perspective for RE research. The experimental results on the FewRel 1.0 and FewRel 2.0 datasets present the superior performance of SACT. SACT not only holds significant relevance for FSRE but also carries broad potential implications for the fields of NLP and RE. It provides valuable insights for future research endeavors in these domains.  
However, there are still some limitations to acknowledge. Firstly, SACT does not consider the \"none of the above\" category, leading to insufficient classification capabilities for similar relationships. Secondly, the model relies solely on predefined relationship categories, making it inflexible to adapt to new or unknown relationship types. In future research, we plan to integrate the SACT model with other large-scale language models possessing robust text comprehension and representation learning capabilities, such as ChatGPT-4, to address SACT's limitations and enhance its classification capabilities for similar and unknown relationship categories. Additionally, we will explore how to apply the SACT model to other NLP tasks, such as few-shot named entity recognition and few-shot text classification.  
Conceptualization, J.M. and J.C.; investigation, Z.S. and Y.C.; software, J.C. and K.L.; writing--original draft preparation, J.C.; validation, F.Z. and Z.S.; writing--review and editing, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the Songshan Laboratory Pre-research Project (YY)C0120 -22023), Henan Province Science Foundation (23230 0420150), Henan Province Collaborative Innovation Center for Internet Medical and Health Services (IH2019006), Zhengzhou University of Light Industry (2021 ZCKJ214), Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness (HNTS202005), and the Science and Technology Innovation Project of Zhengzhou University of Light Industry (233 NKJTD0205)  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/thunlp/FewRel](https://github.com/thunlp/FewRel)]([https://github.com/thunlp/FewRel](https://github.com/thunlp/FewRel)).  
We thank the reviewers for their valuable comments on this paper, which enhanced the quality of the manuscript. At the same time, we sincerely appreciate the guidance and suggestions provided by [PERSON] during the process of revising our manuscript. Her contributions have greatly enhanced the quality of our work.  
The authors declare no conflict of interest.