Entity alignment (EA) aims to identify equivalent entities across different knowledge graphs (KGs). While existing approaches leverage KG neighborhood structures for alignment, they often fail to effectively distinguish relevant from irrelevant neighbors due to insufficient handling of neighbor heterogeneity. Additionally, entity enhancement strategies remain underutilized. To address these issues, we propose a novel Relationship Structure Line Graph for Semi-supervised Entity Alignment Based on Edge Weight Adjustment, named RSGEA. It first enhances entity representations by deeply analyzing relational connectivity structures in KGs, capturing key relational information from second-order and triangular-ring structures. It then employs an attention mechanism to dynamically adjust edge weights, mitigating the impact of noisy edges during information propagation. Finally, we employ the Sinkhorn algorithm to refine the similarity matrix, improving alignment accuracy. Furthermore, we introduce an unsupervised version to accommodate diverse scenarios. Extensive experiments on five cross-lingual datasets validate the effectiveness and robustness of the RSGEA, demonstrating significant performance improvements.