Knowledge graphs have a wide range of applications in areas such as intelligent question and answer, personalized recommendation and intelligent decision making. Knowledge graph representation learning aims to address the sparsity and incompleteness of entities and relationships in knowledge graphs. Traditional representation learning methods based on translation operations such as TransE and TransH usually only consider the triadic information of the knowledge graph to learn the representation in isolation, ignoring the rich textual information, type information, etc., resulting in not mining the semantics carried by the entities in the knowledge graph itself. To address the shortcomings of the existing methods, this paper proposes a representation learning method that fuses entity description and type, introducing entity description and type information on the basis of the SRotatE model, aiming to learn triadic knowledge while fusing triadic factual information with entity description and type information, which can better express the semantics between entities and relationships, thus improving the performance of the knowledge graph representation learning model performance of the knowledge graph representation learning model. Experimental results on real datasets show that the proposed method outperforms mainstream models such as TransE, ComplEx and RotatE.