The work aims to address the problem that large areas of damage are difficult to repair, the perceptual field and feature space information are under-utilized in the repair process, and the structure, texture and style between the repaired cavity areas and the background are inconsistent. An inpainting network FFC-MFMGAN based on Fourier convolution and multi-feature modulation was proposed. Fourier convolution had a large perceptual field in the shallow layer of the network, especially in wide masks, which could skip the mask zone to capture the effective features especially when the mask was wide. The generative network based on the multi-feature modulation was able to enhance the semantic coherence with undamaged regions and the diversity of restoration at large void rates with information from intact regions and random pattern manipulation, respectively. Experiments were conducted to compare the proposed method with other state-of-the-art image restoration methods on the Place 2 dataset, and the following categories were tested to show significant improvements, including a 1.4% improvement in PSNR, a 4.5% improvement in SSIM, a 12.6% reduction in MAE, and a 9.1% reduction in LPIPS. The FFC-MFMGAN network can better repair large irregular holes, while enhancing the global structure and clarity of the repaired images, which is also a reference value for the repair of defects in actual packaging printing images.