Due to the irregular features of the airport pavement cracks with variable shapes， narrow widths， different lengths， and free-form spatial trends， the existing algorithms perform not well. To solve this problem， this paper constructs a neural network model based on deformable convolution and feature fusion （DFNet）. Firstly， the deformable convolution module is used to enhance the learning of fracture morphological characteristics by feature extraction network. Secondly， the multi-scale convolution module captures the global information of the fracture under different receptive fields. Finally， the feature fusion module is used to extract the characteristics of different levels of the fracture， through combining low-level and high-level features of cracks to achieve accurate segmentation of cracks on airport pavements. On the collected actual airport pavement crack data set， comparative experiments are carried out with other six existing algorithms. The proposed algorithm reaches 90.95% on the F1-Score of pixel-level segmentation， which is better than those of all other compared algorithms. The DFNet algorithm improves the ability of detecting cracks in airport pavements. Experimental results show that the proposed algorithm better meets the actual engineering requirements.