Abstract: Ground penetrating radar (GPR) is an important nondestructive testing tool, which is widely used in the fields of road defect detection, subsurface object detection and unstructured terrain perception. To overcome the limitations of the incomplete feature extraction and the low accuracy of deep learning based inversion method, an intelligent inversion algorithm with attention mechanism for GPR image is proposed, and a neural network named InvNet for GPR image inversion is designed to complete the task of nonlinear mapping from GPR image to permittivity image. Firstly, the time dimension convolution and the global feature residual encoder are used to obtain the time dimension and global feature of the GPR image. Then, an improved attention mechanism is used as an attention semantic extraction module to extract global semantic information from the feature map. Finally, the reconstruction of permittivity image is completed by the decoder of permittivity. The GPR inversion dataset is constructed based on the finite difference time domain method and the experiments on the dataset show that the structural similarity index measure (SSIM) of InvNet reached 97.14%, mean square error (MSE) decreased to 0.0030 and mean absolute error (MAE) decreased to 0.015 7. Compared with the latest GPR inversion network algorithms GPRInvNet and PINet, SSIM of InvNet is increased by 1.06% and 0.86%, MSE is reduced by 0.24%, 0.25% and MAE is reduced by 1.41% and 1.02% respectively. The results show that this method can effectively reverse the GPR image to the corresponding permittivity image and the comparison results with the existing methods show its superior inversion performance.