Anomaly detection system plays a significant role in cyberspace security and provides effective protection for network security. Regarding complex network traffic information, the traditional single classifier is often unable to ensure high detection accuracy and strong generalization ability at the same time. In addition, the anomaly detection model based on full features is often disturbed by redundancy features, which affects the accuracy and efficiency of detection. To address these problems, this study proposes a feature selection and ensemble learning model based on average feature importance. The decision tree (DT), random forest (RF), and extra tree (ET) are selected as the base classifiers to establish a voting ensemble model, and the average feature importance of the base classifiers is calculated based on the Gini coefficient for feature selection. The experimental evaluation results on several datasets show that the proposed model is superior to the classical ensemble learning models and other well-known anomaly detection ensemble models. The proposed model can improve the accuracy of the ensemble model by about 0.13% and save about 30% of training time on average.