While community detection techniques reveal latent structures in complex networks, they pose privacy leakage risks. To achieve privacy protection, researchers have focused on community hiding methods that disrupt community structures through network structural perturbations with minimal costs. However, existing topology perturbation-based approaches fail to effectively characterize intrinsic network relationships due to their neglect of node multi-dimensional attribute features. To address this, this paper innovatively constructs an Attribute-enhanced Stochastic Block Model (AESBM) and proposes a Community Hiding Algorithm based on AESBM (HC-AESBM). First, by jointly modeling network topology and node attributes, an Expectation-Maximization algorithm iteratively estimates the node membership matrix, connection probability matrix, and attribute correlation matrix. Second, an edge probability generation model incorporating attribute similarity is established to reconstruct network structure formation mechanisms. Finally, precise perturbations are implemented via a critical edge identification strategy to conceal community structures. Comprehensive experiments on real-world network datasets demonstrate the effectiveness of the proposed method. Results show significant advantages in community hiding metrics: Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Jaccard similarity coefficient decrease by 12.3%, 20.9%, and 11.1% respectively, with an 18.5% reduction in average perturbation cost. These findings validate the effectiveness of attribute information fusion in enhancing community hiding performance.