In view of traditional feature selection methods such as information gain algorithm have preference for selecting features that have more values, Pearson correlation coefficient alone cannot be used to deal with nonlinear correlation, and optimization of algorithm parameters is too tedious, a feature selection fusion approach is proposed based on maximum information coefficient and Pearson correlation coefficient. Moreover, this approach makes use of genetic algorithm to optimize parameters automatically. In the first stage, the feature selection is carried out according to the maximum information coefficient and the correlation between features and tags. In the second stage, Pearson correlation coefficient is used to reduce the redundant acquired features. Furthermore, two hyper-parameters in the first two stages are optimized automatically based on genetic algorithm. The experimental results show that the algorithm can reduce the dimension of feature space and improve the classification performance.