Abstract: Big data processing technology and parallel computing method are used to mine the user behavior characteristics of social network, and the intelligent recommendation of social network is realized. A fusion social network recommendation model based on user behavior mining is proposed. The association rule distribution model is used to detect the user behavior characteristics of the fused social network, and the ontology information and association rules items of the user behavior of the fused social network are extracted, and the fuzzy decision model of the joint recommendation of social network is constructed. The joint information entropy eigenvalue of user behavior is calculated, and the fuzzy C-means clustering method is used to classify and recognize the extracted features. User behavior mining and adaptive recommendation based on social network fusion are realized according to classification and recognition results. The simulation results show that the proposed method has a high precision and a high confidence level.

Key words: social network, user behavior, mining, recommendation, feature extraction