Betweenness centrality is a common metric for evaluating the importance of nodes in a graph. However， the update efficiency of betweenness centrality in large-scale dynamic graphs is not high enough to meet the application requirements. With the development of multi-core technology， algorithm parallelization has become one of the effective ways to solve this problem. Therefore， a Parallel Algorithm of Betweenness centrality for dynamic networks （PAB） was proposed. Firstly， the time cost of redundant point pairs was reduced through operations such as community filtering， equidistant pruning and classification screening. Then， the determinacy of the algorithm was analyzed and processed to realize parallelization. Comparison experiments were conducted on real datasets and synthetic datasets， and the results show that the update efficiency of PAB is 4 times that of the latest batch-iCENTRAL algorithm on average when adding edges. It can be seen that the proposed algorithm can improve the update efficiency of betweenness centrality in dynamic networks effectively.