The multi-signature method can improve the accuracy of entity resolution. However,it will bring the redundant computation problem in the parallel processing framework. In this paper,a multisignature based parallel entity resolution method called multi-sig-er is proposed. The method was implemented in MapReduce-based framework which first tagged multiple signatures for each input object and utilized these signatures to generate key-value pairs,then shuffled the pairs to the reduce tasks that are responsible for similarity computation. To improve the performance,two strategies were adopted. One is for pruning the candidate pairs brought by the blocking technique and the other is for eliminating the redundancy according to the transitive property. Both strategies reduce the number of similarity computation without affecting the resolution accuracy. Experimental results on real-world datasets show that the method tends to handle large datasets rather than small datasets,and it is more suitable for complex similarity computation as compared to simple similarity matching. The multi-signature method can improve the accuracy of entity resolution. However, it will bring the redundant computation problem in the parallel processing framework. In this paper, a multisignature based parallel entity resolution method called multi-sig-er is proposed. was implemented in MapReduce-based framework which first tagged multiple signatures for each input object and utilized these signatures to generate key-value pairs, then shuffled the pairs to the reduce tasks that are responsible for similarity computation. To improve the performance, two strategies were One is for pruning the candidate pairs brought by the blocking technique and the other is for eliminating the redundancy according to the transitive property. Both strategies reduce the number of similarity computation without affecting the resolution accuracy. Experimental results on real-world datasets show that the method tends to handle large datasets rather than small datasets, and it is more suit able for complex similarity computation as compared to simple similarity matching.