Abstract: Knowledge graphs often face the challenge of incompleteness, which can be alleviated by completing missing information through link prediction tasks. However, most knowledge graph completion works overly focus on extracting embedding features without sufficiently considering the complex semantics contained in the predicted node neighborhood information, global feature information, and directional feature information, making it difficult to accurately predict the missing information. This paper proposes a general representation learning semantic enhancement framework, ASFR, which utilizes an attention mechanism to extract local association information of the knowledge graph and structural features of the knowledge graph, and enhances existing knowledge graph representation learning models by incorporating positional information. By embedding these three types of additional knowledge graph information into the entity vectors of the knowledge graph, the quality of the knowledge graph representation vectors is improved. Comparative experiments are conducted using five different categories of classical methods, and the results indicate that this framework can effectively enhance the predictive capability of models, achieving an improvement of 6.89% on three public datasets.