The existing dam deformation prediction model has low accuracy. It is difficult to determine the parameters and structure of the BP neural network and is easy to fall into the local extremum. Therefore, this paper introduces the wavelet transform theory to decompose the original dam deformation sequence into several subsequences, then uses the brain storm optimization(BSO) algorithm to optimize the parameters and structure of the BP neural network for each subsequence. At the same time, this paper applies the differential mutation idea to the basic BSO algorithm and establishes a dam deformation prediction model based on the wavelet transform and differential mutation BSO algorithm for optimizing the BP neural network. The results of the present study indicate that the proposed prediction model has higher prediction accuracy than other prediction models.