  We propose a novel technique to enhance Knowledge Graph Reasoning by
combining Graph Convolution Neural Network (GCN) with the Attention Mechanism.
This approach utilizes the Attention Mechanism to examine the relationships
between entities and their neighboring nodes, which helps to develop detailed
feature vectors for each entity. The GCN uses shared parameters to effectively
represent the characteristics of adjacent entities. We first learn the
similarity of entities for node representation learning. By integrating the
attributes of the entities and their interactions, this method generates
extensive implicit feature vectors for each entity, improving performance in
tasks including entity classification and link prediction, outperforming
traditional neural network models. To conclude, this work provides crucial
methodological support for a range of applications, such as search engines,
question-answering systems, recommendation systems, and data integration tasks.
