## 1 Introduction  
Relation extraction has garnered significant attention from researchers, as it is an important subtask in the information extraction task and plays an important role in many downstream natural language processing applications, e.g., sentiment analysis, question answering application, abstract summarization, and knowledge graph construction. If the entity pairs e1 and e2 are labeled and the types of relationships are predefined, the task becomes a standard classification problem. For example, \"[PERSON] made a similar <e1> offer </e1> in a <e2> phone call </e2> made earlier this week.\" In this text, \"offer\" is the head entity e1, \"phone call\" is the tail entity e2, and the relationship between the two entities is of type \"Message-Topic (e2, e1)\". With the emergence of language models, such as BERT [1], RoBERTa [2], GPT [3], etc., their powerful ability to capture contextual information has been proven. Currently, there is a significant amount of work in the field of relational extraction that involves initial fine-tuning of pre-trained language models to obtain vector representations containing rich semantic information. Subsequently, researchers make algorithmic improvements on top of these models. In [4], it is demonstrated that a difference exists in the objective forms of the pre-trained language model (PLM) between the pre-training and fine-tuning phases. Typically, PLMs are usually pre-trained with a cloze-style task. However, in the fine-tuning phase, the vector representation of just one or a few words in the model may be utilized for a specific task. For example, when using the BERT model to do sentiment analysis on the sentence \"This is a great movie\". First of all, a \"[CLS]\" special token is usually added in front of the sentence, which becomes \"[CLS] This is a great movie\". Then, the vector representation of this sentence is obtained by BERT as X = {\(x_{0}\), \(x_{1}\), \(x_{2}\),..., \(x_{t}\)}, where \(x_{0}\) is the representation of the \"[CLS]\" token. Next, a straightforward classification task uses only \(x_{0}\) through a linear layer. This approach may result in a model that only partially leverages the semantic information in the pre-trained language model. For supervised relation extraction models, the quantity and quality of data in the training set significantly impact the final performance of the model. Currently, the training data for such models mainly relies on manual labeling to complete. While manually labeled data is generally reliable, labeling data is time-consuming and labor-intensive. Additionally, obtaining diverse expressions that represent the same semantic information from manually labeled data can be challenging. One of the purposes of data augmentation is to increase the diversity of training data, which can effectively alleviate the problem of data scarcity. When using a pre-trained language model for relation extraction tasks, the final prediction is typically the label with the highest probability from the output probability distribution of the classification layer. Sometimes, the model incorrectly predicts relationships between entities, but the correct labels may be in the first K probability distributions, which contain valuable information for the relationship extraction task. However, it has yet to receive extensive attention for relational classification tasks. Some datasets define relationship categories by distinguishing the relative positions of entities, e.g., \"Entity-Origin (e1, e2)\" and \"Entity-Origin (e2, e1)\" are two different relationship categories. This paper proposes a relation extraction method based on prompt information and Top-K prediction sets to address the above problems. Firstly, adding prompt information before each input data can link the pre-training and fine-tuning phases of the pre-trained language model. The prompt information and Top-K prediction set are effectively fused through the multi-head attention [5] to more fully utilize the rich semantic information of vectors.