Currently, knowledge representation learning has been effectively utilized in knowledge graph completion tasks to predict missing entities or relationships within knowledge graphs, achieving considerable success. However, the structural features of triples after data construction are the only considerations for most traditional completion methods based on representation learning, the results in the rich textual, image, and other multimodal information in the original data not being effectively utilized. To address this problem, it is proposed that the energy function of triples should be defined as the sum of sub-energy functions, which are characterized by the textual features of relationships and the multimodal features of entities, to fully exploit the semantic information in the triple. In addition, a multimodal negative sampling strategy has been devised for diverse energy functions, aiming to align the disparate features of entities, enhance the quality of knowledge representation, and boost the performance of knowledge graph completion. Link prediction experiments were conducted on publicly available datasets DB15K and FB15K-237. The results show that the proposed model exhibits remarkable capabilities in knowledge graph completion.