The present disclosure provides an unbalanced sample classification method and an unbalanced sample classification apparatus. The method includes: obtaining unbalanced sample data; calculating a sample contribution rate based on the sample data and the characteristic data; filtering out a part of the sample data within a preset sample contribution threshold according to the sample contribution rate to determine as target sample data; and inputting the target sample data into a sample classification model to calculate a sample classification result through a classification algorithm. By using two variables of the characteristic value contribution rate and the characteristic contribution rate, the characteristics and samples with low contribution rate for classification are eliminated to effectively reducing the processing of unbalanced sample data, and a machine learning classification algorithm can be used on this basis to adopt the effective characteristics or samples to achieve efficient classification.