Density clustering can effectively identify clusters of different shapes and densities and has received extensive attention in the field of data mining. However, how to efficiently calculate local densities and accurately identify density peaks remains a key issue faced by density clustering. To this end, this paper proposes an adaptive density clustering algorithm based on neighborhood expansion. First, calculate the dynamic inverse nearest neighbor sequence of each data object; Secondly, calculate the corresponding dynamic information entropy based on the dynamic inverse nearest neighbor sequence, and determine the density center through the information entropy. Finally, adaptive clustering is achieved by dynamically determining the local density centers through iterative neighborhood expansion. The experimental results show that this algorithm can not only accurately identify the density centers of the data set, but also adaptively discover clusters of various shapes and densities, and has good robustness against noise and variable density. Compared with the DBSCAN, DPC and RNN-DBSCAN clustering algorithms, the algorithm in this paper achieves better clustering results.