Feature selection plays an important role in the classification accuracy and generalization performance of classifiers. The existing multi-label feature selection algorithms mainly use the maximum relevance and minimum redundancy criterion to perform feature selection in all feature sets without considering expert features， therefore， the multi-label feature selection algorithm has the disadvantages of long running time and high complexity. Actually， in real life， experts can directly determine the overall prediction direction based on a few or several key features. Paying attention to and extracting this information will inevitably reduce the calculation time of feature selection and even improve the performance of classifier. Based on this， a multi-label feature selection algorithm based on conditional mutual information of expert feature was proposed. Firstly， the expert features were combined with the remaining features， and then the conditional mutual information was used to obtain a feature sequence of strong to weak relativity with the label set. Finally， the subspaces were divided to remove the redundant features. The experimental comparison was performed to the proposed algorithm on 7 multi-label datasets. Experimental results show that the proposed algorithm has certain advantages over the other feature selection algorithms， and the statistical hypothesis testing and the stability analysis further illustrate the effectiveness and the rationality of the proposed algorithm.