Existing knowledge representation methods only consider triplet itself or one kind of additional information, and do not make use of external information to semantic supplement knowledge representation. The convolutional neural network was used to extract feature information from text. The convolutional neural network based on attention mechanism was used to distinguish the feature reliability of different relationships, enhance the representation of entity relationship structure vector in the existing knowledge graph and obtain rich semantic information. A weighted hierarchical encoder which combined all the hierarchical type projection matrix of the entity with the relationship-specific type constraints, was used to construct the projection matrix of the hierarchical type, The link prediction and the triplet classification were performed on WN18, WN18RR, FB15K, FB15K-237 and YAGO3-10 datasets to analyze and verify the validity of the proposed model. The experiment showed that in the entity prediction experiment, the proposed model reduced the MeanRank(Filter) by 11.8% compared to the TransD model, and increased Hits@10 by 3.5%. In the triple classification experiment, the classification accuracy of the proposed model was 8.4% higher than the DKRL model and 8.5% higher than the TKRL model, which fully proved that the ability of knowledge representation could be improved by using external multi-source information.