Traditional shallow neural networks exhibit low prediction accuracy and poor generalization when handling high-dimensional data. To solve these problems, this study proposes an intelligent control algorithm for high-rise buildings based on one-dimensional convolutional neural networks(1D-CNN) and the deep dream visualization algorithm. The proposed method enables high-precision network model training and visualizes data features through 1D-CNN. Using a 20-story benchmark model as a case study, the damping performance of the 1D-CNN-based intelligent control algorithm was analyzed under different conditions and compared with back propagation(BP) and radial basis function(RBF) algorithms. Results show that 1D-CNN can effectively extract deep data features and reduce the dimensionality of massive datasets by virtue of one-dimensional convolution and pooling operations. Under external excitation, the maximum damping rates for acceleration and displacement achieved by the 1D CNN controller were 69.0% and 55.6% respectively, significantly outperforming BP and RBF. Although the control performance of all algorithms decreased under modified excitation conditions, the 1D-CNN consistently exhibited superior performance and the best generalization capability.