The precision of user identification is low since the subjective weighting algorithms ignore the special meanings and effects of attributes in applications. To solve this problem, an Information Entropy based Multiple Social Networks User Identification Algorithm (IE-MSNUIA) was proposed. Firstly, the data types and physical meanings of different attributes were analyzed, then different similarity calculation methods were correspondingly adopted. Secondly, the weights of attributes were determined according to their information entropies, thus the potential information of each attribute could be fully exploited. Finally, all chosen attributes were integrated to determine whether the account pair was the matched one. Theoretical analysis and experimental results show that, compared with machine learning based algorithms and subjective weighting algorithms, the performance of the proposed algorithm is improved, on different datasets, the average precision of it is up to 97.2%, the average recall of it is up to 94.1%, and the average comprehensive evaluation metric of it is up to 95.6%. The proposed algorithm can accurately identify user accounts across multiple social networks.