In order to solve the problem of real-time monitoring and accurate prediction of structural deformation of platform doors on high-speed railway lines, an artificial intelligence-based neural network method was used. Structural deformation data of platform doors, involving 210 different conditions of train length, blocking ratio, installation distance, and speed, were selected as training samples for the network model. Two neural network models, CNN(convolutional neural network) and K-Fold(K-Fold cross-validation) optimized GRNN(general regression neural network), were used to establish predictive models for platform door structural deformation under different working conditions of high-speed railways. These models were compared and verified with the remaining sample data. The research shows that both models effectively predict the operation and maintenance data of railway platform door structures. The K-Fold optimized GRNN model is superior to the CNN model in prediction accuracy. The Mean Square Error of the K-Fold optimized GRNN model is maintained within 0.22, and theRoot Mean Square Error is within 0.27, which is at the leading level in the field. The K-Fold optimized GRNN model better predicts the structural deformation of platform doors when trains pass, providing data references for the design and maintenance of high-speed railway platform doors.