Attributed graphs are increasingly used to represent data with relational structures, and detecting anomalies with them is gaining attention. Due to their characteristics, such as rich attribute information and complex structural relationships, various types of anomalies may exist, including global, structural, and community anomalies, which often remain hidden within the graph’s deep structure. Existing methods face challenges such as loss of structural information and difficulty identifying abnormal nodes. Structural information theory leverages encoding trees to represent hierarchical relationships within data and establishes correlations across different levels by minimizing structural entropy, effectively capturing the graph’s essential structure. This study proposes an anomaly detection method for attributed graphs based on structural entropy. First, by integrating the structural and attribute information of attributed graphs, a K-dimensional encoding tree to represent the hierarchical community structure through structural entropy minimization is constructed. Next, using the node attributes and hierarchical community information within the encoding tree, scoring mechanisms for detecting structural and attribute anomalies based on Euclidean distance and connection strength between nodes are designed. This approach identifies abnormal nodes and detects various types of anomalies. The proposed method is evaluated through comparative tests on several attributed graph datasets. Experimental results demonstrate that the proposed method effectively detects different types of anomalies and significantly outperforms existing state-of-the-art methods.