To speed up the optimization of antenna modeling, an improved one-dimensional convolutional neural network ( 1D-MCNN ) model is proposed. The convolution kernel size of this one-dimensional neural network is 2, and the ReLU function is used as the activation function to reduce the gradient dispersion. The Adam optimizer is combined with dropout technology to improve the feature learning ability and nonlinear function approximation ability of the model. In this paper, the 1D-MCNN model is used to model the geometric parameters of the ultra-wideband microstrip monopole antenna. The eight geometric parameters of the antenna are used as feature inputs to predict the return loss value of the antenna. Experiments show that compared with the deep MLP network model, MLP network model, and RBF neural network model, the average error of the return loss value of the 1D-MCNN model proposed in this paper is reduced by 1. 95%, 120. 27%, and 125. 71% respectively. It has higher accuracy and stronger prediction ability. It is feasible to optimize the modeling of ultra-wideband antennas and has certain advantages.