With the advent of the data era， the classification of unbalanced data is receiving more and more attention. In the classification of unbalanced data， classification results are often incorrect due to an imbalance in the ratio of minority class samples to majority class ones. Therefore， we propose an oversampling algorithm based on the Bootstrap method under the maximum entropy principle. Firstly， the probability distribution of the data sample is obtaited through self-help method and optimized using the principle of maximum entropy. Secondly， a probability enhancement algorithm based on minority class sample distribution is proposed based on different abilities of minority classes to generate new minority classes. The algorithm allows the randomness of the data to be fully represented and ensures that the probability density of the minority class remains consistent before and after the data set is balanced， thus improving the effectiveness of the classification algorithm. Finally， experiments are conducted by selecting eight data sets from the UCI and KEEL databases， whose results show that the proposed algorithm is more effective than other algorithms.