<p>Dynamic updating of intelligence knowledge graphs has emerged as a significant research topic for wide range of applications. However, as intelligence data continuously accumulates, dynamic update process of knowledge graph faces the inaccuracy problem, caused by complexity of incremental data and noise interference. To address the issue, we propose a novel Graph Embedding-based Dynamic Update Method (GEDUM) for intelligence knowledge graphs, which comprehensively considers the dynamic evolution characteristics of intelligence data and optimizes the updating of knowledge graph through embedding networks. Specifically, we design a Local-to-Global Feature Aggregation Module (L2GFAM) for learning global graph embeddings, deeply exploring and optimizing intrinsic features of graph nodes and edges. Building on this, an Attention-guided Weighted Fusion Strategy (AWFS) is proposed to efficiently merge and update embeddings of local subgraphs and newly added graph components, taking into account the correlation and complementarity between new and existing data. Extensive validations on real-world dataset demonstrate the significant superiority of our proposed solution over traditional methods in handling dynamically evolving intelligence data.</p>