Research on Similarity Feature Search of Network Users Based on Dynamic Clustering Analysis

The similarity features of network users reflect the preferences of users, in order to improve the adaptive recommendation ability of network information, A similarity feature search algorithm for network users based on dynamic clustering analysis is proposed. Non-stationary random sequence analysis method is used to construct the network user information transfer model, and the association rule feature quantity which reflects the network user behavior feature is extracted, and the association information annotation method is used to retrieve the network user similarity behavior characteristic information. Based on the dynamic clustering analysis of the extracted network users' similarity feature information, the fuzzy clustering method is used for adaptive classification and recognition of the similarity behavior feature information, so as to improve the ability of adaptive searching and information processing of the network users' similarity features. The simulation results show that the proposed method is more accurate in searching network users' similarity features, and it has a better ability of clustering and recognition of network users' similarity behavior information, thus improving the ability of optimizing and recommending network information.