As imbalanced data are exposed to problems such as intra-class imbalance, noise, and small coverage of generated samples, an adaptive denoising hybrid sampling algorithm based on hierarchical density clustering (ADHSBHD) is proposed. Firstly, the clustering algorithm HDBSCAN is introduced to perform clustering on minority classes and majority classes separately; the intersection of global and local outliers is regarded as the noise set, and the original data set is processed after noise samples are eliminated. Secondly, according to the average distance between clusters of samples in minority classes, the adaptive sampling method with broader coverage is used to synthesize new samples. Finally, some points that contribute little to the classification of majority classes are deleted to balance the dataset. The ADHSBHD algorithm is evaluated on six real data sets, and the results can prove its effectiveness.