The common strategy adopted by most existing multi-label learning algorithms in model training is to predict all the label categories based on the same label feature set. However, this idea does not take into account the label-specific features of each label, which are very helpful for distinguishing other categories of labels and describing itself in the label space. For this reason, an improved ML-KNN algorithm based on label-specific features, i.e., MLF-KNN, is proposed in this study. Different from the previous multi-label algorithms which directly operate on the original training data set, the algorithm proposed in this study first builds features for each category of label by preprocessing the training data set. Then, it further constructs and optimizes L₁-norm in the obtained label space, thus introducing the correlation between labels. Finally, the improved algorithm is applied for prediction and classification. The experimental results show that the improved algorithm has achieved certain advantages compared with the ML-KNN algorithm and other three multi-label learning algorithms on the public image and yeast data sets.