Existing knowledge graph update approaches often suffer from high computational cost, long update latency, and difficulties in maintaining consistency, which limit their applicability in large-scale dynamic environments. To address these issues, an incremental update method based on core entities and local subgraphs was proposed. A weighted centrality metric was constructed by integrating entity degree centrality with business access frequency, and the top-K entities with the highest scores were selected as core entities. In this way, the update process was focused on the entities that were most influential to downstream applications. Local subgraphs associated with the identified core entities were extracted through k-hop neighborhood expansion and edge-weight filtering, so that the update scope was restricted to semantically relevant structures and redundant computation was reduced. During model optimization, a local objective function was established for fine-tuning pre-trained knowledge graph embeddings. A neighborhood stability regularization term was introduced to suppress embedding drift caused by incremental updates. In addition, Schema constraints and multi-source confidence fusion were incorporated to ensure the consistency and reliability of the updated graph. Experiments were conducted on large-scale knowledge graph datasets. The results showed that, compared with full graph reconstruction and conventional fine-tuning methods, the proposed method reduced update latency by approximately 60%, improved triple insertion accuracy by approximately 8 percentage points, and decreased the consistency conflict rate by approximately 12 percentage points. These results indicate that the proposed method can effectively improve update efficiency while maintaining the structural and semantic quality of the knowledge graph.