A fine-tuned and filtered oversampling method based on Agglomerative Hierarchical Clustering （AHC） was proposed to address the issue of poor classification performance on imbalanced datasets， which can be applied to multi-class imbalanced data scenarios. Firstly， AHC algorithm was employed during the clustering process of imbalanced datasets， so that the majority and minority classes were clustered separately， thereby avoiding class overlap effectively while considering inter-class relationships. Secondly， to balance the dataset while preserving characteristics of the original data， a fine-tuned oversampling algorithm was designed. Thirdly， to improve classification accuracy of the generated samples， a label tendency evaluation and filtering method based on propensity score matching was introduced. Finally， the proposed method was validated through experiments and compared with three methods： MDO （Mahalanobis Distance-based Over-sampling technique）， AND-SMOTE （Automatic Neighborhood size Determination method for Synthetic Minority Over-sampling TEchnique）， and K-means SMOTE. Experimental results demonstrate that the proposed method has excellent performance on six different datasets such as Abalone， Contraceptive and Yeast， confirming effectiveness of the method.