In view of the problem that traditional resampling methods mostly use fixed sampling strategies and cannot change the sampling strategy according to the optimization requirements of the model, this paper proposes an adaptive sampling-based imbalanced classification method (Adaptive Sampling Imbalanced Classification, ASIC). This method dynamically adjusts the sampling probabilities of samples of different classes on the training set according to the performance of the classification model on the validation set, so that the sampling probabilities of different classes are dynamically determined by the requirements of the current classification model. At the same time, this method pays extra attention to the minority classes, and gives the minority classes a higher sampling probability under the same other conditions, so as to compensate for the negative impact of the insufficient example number of the minority class itself on the classification model, thereby improving the classification model's ability to recognize minority classes. The experimental results show that the classification model trained with the ASIC method is better than the comparison methods in terms of balanced accuracy and geometric mean, and the more imbalanced the data distribution, the more obvious the superiority of the ASIC method.