In the task of detecting targets under airport pavement， B-scan maps generated by Ground Penetrating Radar （GPR） have complex backgrounds and lots of noise， especially a single B-scan map cannot reflect the complete information of an underground target. To solve these problems， a Three-Dimensional Channel and Spatial Attention UNet （3D-CSA-UNet） model was established to automatically detect the underground targets. Firstly， a Three-Dimensional Channel and Spatial parallel attention Block （3D-CS-Block） was designed to make the model focus on the underground target information in radar C-scan and suppress the interference of backgrounds and noise. Secondly， in order to enhance the capability of 3D-CS-Block in feature extraction， a multi-scale 3D segmentation model was designed to extract feature maps of different sizes from the radar C-scan. Finally， the cross-entropy loss function was employed to calculate the loss value of feature map under each scale to improve the detection accuracy of the model. On a real dataset of targets under airport pavement， compared with 3D-Fully Convolutional Network （3D-FCN）， 3D-UNet and other algorithms， 3D-CSA-UNet has the average F1 score in terms of the pixel level segmentation for void， rebar and parallel rebar targets increased by at last 12.33， 9.05 and 11.05 percentage points. Experimental results show that 3D-CSA-UNet can meet the real engineering requirements well.