<p>Auditing is facilitated by audit knowledge graphs, while the biggest challenge in constructing an audit knowledge graph is entity alignment. Entity alignment involves linking entity pairs with the same real-world identity and aims to integrate heterogeneous knowledge across different knowledge graphs. However, most existing works do not effectively combine both attribute and relation representations into a unified framework for entity alignment, which is essential to link entities within an audit knowledge graph accurately. In this study, we propose a knowledge graph-based entity alignment approach with multi-attribute and weighted-relation fusion (KG-Marfia) for intelligent auditing. Our proposed KG-Marfia first extracts entity representations by addressing the imbalance of attributes and relations, and then designs a stacked graph convolutional network as an encoder to fuse attribute and relation information, learning unified representations for entities. In particular, we adopt an SVM-based classifier for the alignment task in intelligent auditing. Experiments conducted on two public datasets, as well as three audit datasets, demonstrate that our KG-Marfia outperforms state-of-the-art entity alignment methods.</p>