By removing irrelevant features from the original dataset and selecting good feature subsets， feature selection can avoid the curse of dimensionality and improve the performance of learning algorithm.In the process of feature selection， only the dynamically change information between the selected features and classes is considered， and interaction relevance between the candidate features and the selected features is ignored by Dynamic Change of Selected Feature with the class （DCSF） algorithm. To solve this problem， a Dynamic Relevance based Feature Selection （DRFS） algorithm was proposed. In the proposed algorithm， conditional mutual information was used to measure the conditional relevance between the selected features and classes， and interaction information was used to measure the synergy brought by the candidate features and the selected features， so as to select relevant features and remove redundant features then obtain good feature subsets. Simulation results show that， compared with existing algorithms， the proposed algorithm can effectively improve classification accuracy of feature selection.