Aiming at the low utilization rate of attribute information and the lack of mining of the correlation among attributes of the existing cross-social network user identity matching algorithms, we proposed an algorithm for user identity matching across social networks utilizing fuzzy measure and Choquet integrals. Firstly, according to the characteristics of different attributes, we determined different similarity calculation strategies; Secondly, we utilized particle swarm optimization method to calculate the fuzzy density of each attribute; Then Choquet integral was utilized to calculate the similarity of two accounts; Finally, the similarity was compared with the preset matching threshold and the final matching result was obtained. The experimental results in multiple sets of data showed that the average F1 value of the proposed algorithm reaching 84.5%. The performance is not only better than traditional machine learning methods, but also better than several baseline algorithms. It can be more accurate to identify the same user's accounts in multiple social networks according to the attribute information.
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