Concerning the high time complexity of traditional heuristic search-based multi-label feature selection algorithm, an Easy and Fast Multi-Label Feature Selection (EF-MLFS) method was proposed. Firstly, Mutual Information (MI) was used to measure the features and the correlations between the labels of each dimension; then, the obtained correlations were added up and ranked; finally, feature selection was performed according to the total correlation. The proposed method was compared to six existing representative multi-label feature selection methods such as Max-Dependency and Min-Redundancy (MDMR) algorithm, Multi-Label Naive Bayes (MLNB) method. Experimental results show that the average precision, coverage, Hamming Loss and other common multi-label classification indicators are optimal after feature selection and classificationby using EF-MLFS method. In addition, global search is not required in the method, so the time complexity is significantly reduced compared with MDMR and Pairwise Mutli-label Utility (PMU).