## 1 Introduction
Despite introducing relationship information to constrain prototypes for better representations, there are limitations to consider: Firstly, they overlook the interactivity among prototypes, which would constrain the model's access to global information within the data. Each prototype network merely learns from its own limited instances, lacking a comprehensive perspective from other prototype networks. This limitation may result in the model's inability to capture potential correlations and shared features among distinct prototypes, leading to a diminished expressive capability of the prototype network. In practical applications, variations in unknown categories or samples may not be confined to a single prototype network but rather involve interactions among multiple prototype networks. Neglecting such interactions could render the model less robust when confronted with unfamiliar situations, making generalization to broader data distributions challenging. As shown in Figure 2, the interaction information between different prototypes can provide strong supporting evidence for RE. Additionally, when employing contrastive learning or graphs to constrain prototypes, there is limited utilization of global information. Consequently, this model may exhibit poor classification performance for outlier samples with low semantic similarity due to substantial differences between their prototypes. To tackle the previously mentioned concerns, this paper presents a relationship-prototype fusion method based on a multi-head self-attention network (SACT). SACT leverages prototype interrelationships more effectively and combines them with relationship description information to generate improved prototype representations. Specifically, SACT introduces a prototype enhancement module that enhances prototype representations by adding a multi-head self-attention mechanism based on the relationships between prototypes. This results in enriched prototype representations that are integrated with the basic prototypes. Furthermore, SACT employs an adaptive space fusion technique to merge relationship information with prototype representations, ultimately obtaining the final prototype representations. This approach facilitates the acquisition of more comprehensive and effective prototypes by the model. Additionally, SACT introduces a contrastive-center loss  
Figure 2: An illustration of the impact of prototype interaction information on query instances. Gray spheres represent prototype networks, while spheres of other colors represent representations of support instances. The green spheres with question marks represent representations of query instances. (**a**) Originally, the representation of the query instance closely resembles the blue prototype. (**b**) After interacting with information from different prototypes, the position of the query instance representation changes, thereby modifying the prototypes. function, which simultaneously minimizes distances among the same class while increasing distances between different classes, thereby improving the handling of outlier samples. The following outline the principal contributions of this paper:  
(1) This paper presents a novel prototype network with multi-head self-attention enhancement with contrast-center loss, named SACT. This model leverages interaction information among sufficient prototypes to enhance them. It employs an adaptive fusion mechanism to integrate relationship information with the improved prototypes, thereby enhancing the classification accuracy. (2) This paper introduces a contrastive-center loss function that enhances intra-class cohesion and inter-class separability by comparing the distances between query samples and their respective class centers with the distances to non-corresponding class centers. (3) Extensive experiments were conducted using two extensive FSRE datasets, FewRel 1.0 and FewRel 2.0, which yielded results surpassing those of other SOTA models. Furthermore, ablation experiments were carried out to showcase the efficacy of SACT.