Objective Class imbalance and concept drift are two main challenges in data stream classification tasks. When they occur simultaneously they significantly affect the performance of data stream classification algorithms. Therefore to address the difficulty of traditional data stream classification algorithms in handling the simultaneous occurrence of class imbalance and concept drift a resampling ensemble model focused on imbalanced data streams was proposed. Methods Firstly a boundary oversampling method tailored for data streams was designed. By leveraging the characteristics of the triangular center of gravity new samples were synthesized inside boundary samples to enhance the minority class within the block while striving to maintain the original data distribution and avoid introducing new concepts. This effectively improved the class imbalance in the data block. On this basis a dynamic weighted ensemble model based on Matthews correlation coefficient as weights was designed by integrating the time decay strategy and weighted ensemble strategy. This model solved the problem of concept drift and enhanced the adaptability and robustness of the classification mining model.