To address the problem of complex background clutter and interference of strong inter-layer reflection in airport runway Ground Penetrating Radar （GPR） data， an improved U-Net-based deep learning algorithm for clutter suppression was proposed. In the algorithm， a detail enhancement module DE-Conv was introduced at the skip connections of U-Net to improve the network ability to capture details of target signals in multi-scale shallow features， and a feature-pixel dual-level fusion loss function was adopted using clutter-contaminated and clutter-free image pairs to optimize the training process. Specifically， high-dimensional features from both clutter-contaminated and clutter-free data were extracted by a shared-weight encoder， feature-level losses were computed to guide the training of the encoder， and pixel-level losses were computed using the images output by the decoder and the corresponding clutter-free simulated images to optimize the decoder’s performance. Experimental results show that the proposed algorithm achieves a Peak Signal-to-Noise Ratio （PSNR） of 37.114 7 dB and a Structural SIMilarity （SSIM） of 0.999 8 on a synthetic dataset， and an average Signal-to-Clutter Ratio （SCR） of 8.28 dB and Improvement Factor （IF） of 5.90 dB on a real airport runway dataset， outperforming the baseline model by 0.952 8 dB， 0.000 4， 6.58 dB， and 5.32 dB in these four metrics， respectively. Compared with Robust Nonnegative Matrix Factorization （RNMF）， Robust Principal Component Analysis （RPCA）， and the deep learning-based Clutter-removal Neural Network （CR-Net）， the proposed algorithm has superior clutter suppression performance and computational efficiency. At the same time， a lot of ablation experimental results validate the effectiveness of the detail enhancement module and feature-pixel dual-level loss function to clutter removal and target signal recovery.