Abstract: Deformation prediction model provides an essential support for the analysis of dam structure safety. However, the existing dam deformation prediction models suffer from low precision and insufficient generalization. To solve this problem, an adaptive fuzzy neural network was introduced into the dam deformation prediction model in this study. Specifically, the particle swarm optimization (PSO) algorithm with dynamic weights was applied to optimize the parameter of fitness values in the fuzzy layer of the adaptive fuzzy neural network. Based on this, an adaptive fuzzy neural network called DPSO-ANFIS was formed, to search for the optimal fitness value. And a dam deformation prediction model based on DPSO-ANFIS was established. Experiments were conducted to verify the effectiveness of the proposed deformation prediction model. The monitoring data from the dam prototype was applied to the trained model to obtain the output values, and these values were then compared with the actual monitoring data. The engineering practice showed that the maximum deviation between the output values of the dam deformation prediction model based on DPSO-ANFIS and the measured values was 0.051 6 mm; the RMSE (root mean square error) was 0.035 1, and the average absolute error was 0.032 0. All the values of these performance indicators were better than those of the dam deformation prediction models based on PSO-ANFIS, ANFIS, and BP neural network. As for the measuring points and time, the predicted values from the dam deformation prediction model based on DPSO-ANFIS exhibited a trend much close to that of measured values. Also, the proposed deformation prediction model achieved a stable overall prediction performance. Therefore, the dam deformation prediction model based on DPSO-ANFIS achieves high accuracy, good generalization, strong stability, and excellent comprehensive performance in practical engineering applications.