Abstract: A random walk algorithm compulsive evolution with individual reconstruction strategy is proposed to solve the problem that the optimization of mass exchanger network is easily trapped in local extremum due to the weakening of the ability of structural variation and the loss of population diversity. In the process of receiving differential solutions, real-time monitoring is carried out on individuals, and different reconstruction methods are adopted to stimulate the network structure updating and variation of backward individuals, so as to improve the structural variation ability and population diversity of the algorithm. At the same time, according to the characteristics of the unstructured model with shunt nodes, a new individual network structure after cross reconstruction is repaired. Finally, the R2S2 and R4S2 examples are used to verify the effectiveness of the proposed strategy, and the optimization results are all lower than the results in the current literature, which proves that the proposed strategy can effectively enhance the structural variation ability and global optimization ability of the algorithm.