The binary brain network modularization is not enough to describe physiological features of human brain. In order to solve the problem, a modularization algorithm for weighted brain network based on Fast Newman binary algorithm was presented. Using the hierarchical clustering idea of condensed nodes as the base, a weighted modularity indicator was built with the main bases of single node's weight and entire network's weight. Then the modularity increment was taken as the testing index to decide which two nodes should be combined in weighted brain network and realize module partition. The proposed method was applied to detect the modular structure of the group average data of 60 healthy people. The experiment results showed that, compared with the modular structure of the binary brain network, the brain network modularity of the proposed method was increased by 28% and more significant difference between inside and outside of modules could be revealed. Moreover, the modular structure found by the proposed method is more consistent with the physiological characteristics of human brain. Compared with the other two existing weighted modular algorithms, the proposed method can also slightly improve the modularity and guarantee a reasonable identification for human brain modular structure.