Abstract: Radar beam blockage is an important error source that affects the quality of weather radar data. The S-band dual-polarization radar in Guangzhou has multi-azimuth occlusion at low elevation and is partially occluded at high elevation. Based on deep learning methods such as convolutional neural network, two echo filling networks, i.e., VEF(vertical echo-filling) and HEF(horizontal echo-filling) are constructed. Based on this architecture, echoes from the unblocked area are used to construct training datasets and fill the reflectivity Z_H and differential reflectivity Z_DR in the occlusion area. For the area with only 0.5° elevation occlusion, multi-modal modeling is carried out based on VEF architecture by using 3D data from multiple upper elevations, radial directions and gates. Considering that the radar beam broadens with distance and to avoid the influence of the melting layer, the radar beam is divided into four sections according to the oblique distance of 0.5° elevation, and the vertical echo-filling model is trained respectively. For the area with high occlusion elevation, multi-mode modeling is carried out based on HEF architecture using the data of multiple adjacent radial directions and gates with the same elevation. According to the number of occlusion radial, two types of horizontal echo-filling models, three radials echo-filling model and five radials echo-filling model are constructed respectively. Finally, the models are evaluated by three cases and three indicators:Explained variance, mean absolute error and correlation coefficient. The maximum explained variance of Z_H vertical echo-filling model is 0.91, the minimum mean absolute error is 1.72 dB, and the maximum correlation coefficient is 0.96. The maximum explained variance of Z_DR vertical echo-filling model is 0.87, the minimum mean absolute error is 0.12 dB, and the maximum correlation coefficient is 0.92. The maximum explained variance of Z_H horizontal fill model is 0.92, the minimum mean absolute error is 1.69 dB, and the maximum correlation coefficient is 0.96. The maximum explained variance of Z_DR horizontal echo-filling model is 0.92, the minimum mean absolute error is 0.12 dB, and the maximum correlation coefficient is 0.96. The deep learning echo-filling model can be used to correct the echoes of Guangzhou S-band dual-polarization radar occlusion area effectively, and the quality of weather radar data is improved.