The data entities stored in large-scale decentralized institutions have issues such as data redundancy， missing information， and inconsistency， which requires integration through entity alignment. Most existing entity alignment methods rely on structural information of entities and perform alignment through subgraph matching. However， the lack of structural information in decentralized data storage will lead to poor alignment results. To address this issue and support identification of important data， a single-layer graph neural network-based attribute-based entity alignment model was proposed. Firstly， a single-layer graph neural network was utilized to avoid interference from secondary neighbor node information. Secondly， an attribute weighting method based on information entropy was designed to distinguish importance of the attributes in the initial stage quickly. Finally， an attention mechanism-based encoder was constructed to represent importance of different attributes in alignment from both local and global perspectives， thereby providing a more comprehensive representation of entity information. Experimental results indicate that on two decentralized storage datasets， the proposed model improves the Hits@1 by 5.24 and 2.03 percentage points， respectively， compared to the suboptimal models， demonstrating superior alignment performance of the proposed model over other entity alignment methods.