With the rapid growth of the number of Android applications, security detection of Android applications has become one of the hot issues in the field of network security. Aiming at the feature selection of static detection for malicious applications, this paper gives the concepts of benign feature, malicious feature, benign typical feature, malicious typical feature and atypical feature, and designs the feature Frequency Differential Enhancement (FDE) algorithm. The FDE algorithm eliminates the atypical features in static features by calculating the frequency of features in benign and malicious applications. In order to verify the target effect and performance of the FDE algorithm, experiments based on equilibrium data and non-equilibrium data are designed, and a weight loss function is introduced for non-equilibrium data experiments. Experimental results show that the FDE algorithm can effectively remove atypical features from static features and screen out valid features, and weight loss function can effectively improve the recognition rate of malicious data in non-equilibrium data.