In order to solve the problems of feature selection ReliefF algorithm， such as poor algorithm stability and low classification accuracy for selected feature subsets caused by using Euclidean distance to select the nearest neighbor samples， an MICReliefF （Maximum Information Coefficient-ReliefF） algorithm based on Maximum Information Coefficient （MIC） was proposed. At the same time， the classification accuracy of the Support Vector Machine （SVM） model was used as the evaluation index， and the optimal feature subset was automatically determined by multiple optimizations， thereby realizing the interactive optimization of the MICReliefF algorithm and the classification model， that is the MICReliefF-SVM automatic feature selection algorithm. The performance of the MICReliefF-SVM algorithm was verified on several UCI public datasets. Experimental results show that the MICReliefF-SVM automatic feature selection algorithm cannot only filter out more redundant features， but also select the feature subsets with good stability and generalization ability. Compared with Random Forest （RF）， max-Relevance and Min-Redundancy （mRMR）， Correlation-based Feature Selection （CFS） and other classical feature selection algorithms， MICReliefF algorithm has higher classification accuracy.