Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multimodal knowledge graphs (MMKGs).Existing methods have made substantial advancements in enhancing multi-modal fusion.However, the intrinsic noise within modalities, such as the inconsistency in visual modality and redundant attributes, has not been thoroughly investigated.Excessive noise not only weakens semantic representation but also increases the risk of overfitting in attention-based fusion methods.To address this, we propose LGEA (LLM-Guided Entity Alignment), a novel LLM-guided MMEA framework that prioritizes noise reduction before fusion.Specifically, LGEA introduces two key strategies: (1) fine-grained visual filtering to remove irrelevant images at the semantic level, and (2) contextual summarization of attribute information to enhance entity semantics.To our knowledge, we are the first work to apply LLMs for both visual filtering and attribute-level semantic enhancement in MMEA.Experiments on multiple benchmarks, including the noisy FBYG dataset, show that LGEA sets a new state-of-the-art (SOTA) in robust multi-modal alignment, highlighting the potential of noiseaware strategies as a promising direction for future MMEA research 1 .