In imbalanced datasets, the presence of noise and class overlapping often leads to poor performance of traditional classifiers, resulting in minority class samples being difficult to classify accurately. To improve classification performance, a method for handling imbalanced data based on shared nearest neighbor density peak clustering and ensemble filtering mechanism is proposed. This method first uses the shared nearest neighbor density peak clustering algorithm to adaptively divide the minority class samples into multiple clusters. Then, based on the density and size within the clusters, oversampling weights are allocated to each cluster. During the synthesis within clusters, the local sparsity and clustering coefficient of the samples are considered to select neighboring samples and determine the weight range of linear interpolation, thus avoiding the generation of new samples in the majority class aggregation area. Finally, an ensemble filtering mechanism is introduced to eliminate noise and hard-to-learn boundary samples to regulate the decision boundary and improve the quality of generated samples. Compared with 5 oversampling methods, this algorithm performs better overall on 8 public datasets.