Abstract: This paper proposes a fact prediction algorithm that integrates knowledge graph embedding with a large language mo-del,aiming to address the challenges of judging the authenticity of triples in the field of bidding.In view of the insufficient generalization ability of traditional fact prediction algorithms and the limitations of a single large language model in handling structured knowledge,this paper employs the TransR model to perform low-dimensional embedding representation of the entities and relationships extracted from bidding documents.At the same time,the Qwen 2.5-1.5 B large language model is utilized to extract text semantic features through LoRa fine-tuning,and a deep integration of the two types of information is achieved in the feature-level fusion module.The experiments are conducted on a real bidding dataset.The experimental results show that theproposed method achieves a precision of 86.4%,a recall rate of 93.2%,and an F1 score of 89.7% in the fact prediction task.Compared with traditional knowledge graph embedding algorithms,the F1 score is improved by 14 percentage points,and compared with the method of only fine-tuning the large language model,the F1 score is increased by 11.3 percentage points.

Key words: Knowled gegraph, Fact prediction, TransR, LoRa fine-tuning, Feature-level fusion