Abstract:The cross-sea bridge system is disturbed by external influences, and its deformation is accompanied by chaos. Chaotic identification in bridge deformation monitoring data is realized, delay time of time series is calculated in C-C method, the G-P method is used to obtain the best embedding dimension. Bridge deformation monitoring data is compared with the obtained time delay. Spatial reconstruction lays the foundation for the establishment of the chaotic time series prediction model; the chaotic time series prediction model is established based on the RBF neural network, the horizontal displacement of the bridge deformation is predicted from the measured data, and we conduct comparative analysis of the prediction results of the chaotic time series based on the largest Lyapunov exponent and the measured data. The results shows that the prediction results of chaotic time series based on RBF neural network are better than those of chaotic time series based on maximum Lyapunov index; short-term prediction has good effect.