Density-based clustering can effectively identify clusters with different shapes and densities and has therefore attracted extensive attention in the field of data mining. However, efficient estimation of local density and accurate identification of density peaks remain key challenges for existing density clustering methods. To address these issues, this paper proposes an adaptive density clustering algorithm based on neighborhood expansion. First, the dynamic reverse nearest neighbor sequence of each data object is calculated. Second, dynamic information entropy is derived from the reverse nearest neighbor sequence and used to identify density centers. Finally, adaptive clustering is achieved by iteratively expanding neighborhood and dynamically determining local density centers. Experimental results show that the proposed algorithm can accurately identify density centers and adaptively discover clusters with various shapes and densities. In addition, it shows strong robustness to noise and variable-density data. Compared with DBSCAN, DPC, and RNN-DBSCAN, the proposed algorithm achieves superior clustering performance.