Since the existing multi-label learning algorithms pay less attention to the problem of correlation and imbalance between label sets, this paper proposes a multi-label learning model based on label correlation and imbalance (MLCI). By coupling other label categories to consider the correlation among labels and reducing the imbalance ratio between labels of instances, the learning model is for each label category, and it is an ensemble classifier that combines the current class of binary imbalance classifier with multiple imbalanced classifiers coupled to other labels. In this paper, seven commonly used multi-label algorithms are used as comparison algorithms to classify the four open datasets of yeast, scene, emotions and CAL500. The experimental results show that the MLCI has obvious advantages in accuracy precision, ranking-Loss, macro-averaging AUC and micro-averaging AUC.