The diversified and high-speed development of network traffic presents a great challenge for traffic identification.As an effective method for data dimensionality reduction

the research of feature extraction is of great significance.A secondary traffic feature extraction model is described as the foundation of the secondary feature extraction algorithm of network traffic.The algorithm divides traffic data into several subsets and gathers the features extracted from different subsets.The index of influence is proposed as the reference of feature ranking and extraction.The experiment results show that the secondary traffic feature extraction model has better performance

and the algorithm can identify traffic more accurately with fewer features.