In multi-label learning, feature selection is an effective method to deal with high-dimensional data problems and improve classification performance. However, most of the existing feature selection algorithms are based on the assumption that the label distribution is roughly balanced, and rarely consider the problem of unbalanced label distribution. To solve this problem, this paper proposes a multi-label feature selection algorithm with weakening marginal labels (WML). The algorithm calculates the frequency ratio of positive and negative labels under different labels as the weight of the label, weakens the marginal label by weighting method, and integrates the label space information into the process of feature selection to obtain a more efficient feature sequence, thus improving the accuracy of label description of samples. The experimental results on several datasets show that the proposed algorithm has certain advantages. The effectiveness and rationality of the proposed algorithm are further proved by stability analysis and statistical hypothesis test.