The invention discloses a cloud mask-oriented non-uniform category sample equalization method. The method comprises the steps of 1, data preprocessing and sample acquisition; 2, sample grouping; 3, cloud mask model training; 4, classifier masking and evaluating; 5, iterative training; 6, cloud masking; and 7, mask data post-processing. According to the sample equalization method adopted by the invention, sample imbalance caused by uneven cloud categories in the remote sensing image is effectively solved, and effective identification and segmentation of various types of clouds in the image arerealized, so that the cloud mask precision is improved; the samples are selectively input, so that the influence of missing or wrong detection samples can be highlighted, the small-class samples are enhanced, the features extracted by the deep learning model are effectively adjusted, and the problem of missing detection or false detection of small-class clouds is solved.