Knowledge graphs play a critical role in intelligent systems, but they face persistent challenges of incomplete data acquisition, noisy information, and inefficient inference under dynamic updates. To address these issues, the authors propose a graph-embedding-based framework that integrates three novel components: (1) a neighborhood-enhanced embedding module that captures richer structural semantics, (2) an inference optimization mechanism based on contextual consistency and confidence reweighting, and (3) a dynamic update strategy for efficient incremental learning. Extensive experiments on FB15k-237, WN18RR, and MedKG show clear improvements over state-of-the-art baselines. The proposed framework achieves Mean Reciprocal Rank gains of 8–15% and Hits@10 gains of 3–6%, demonstrating substantial accuracy improvements in link prediction. On dynamic update tasks, the proposed method maintains almost identical accuracy to full retraining (AUC difference < 0.2%) while achieving a 7.7-fold reduction in update time. These results verify that the proposed framework significantly enhances both the effectiveness and efficiency of knowledge graph reasoning.