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In order to solve the problems of complex cracks, high rate of missing cracks and low background contrast in the process of automatic crack identification and extraction of airport runway, a crack detection algorithm based on genetic algorithm and neural network was proposed. First of all, the airport pavement crack images were preprocessed, including image graying, Gaussian filtering and ROI region determination. By setting the network parameters of the genetic algorithm, the selection, crossover and mutation operations are repeatedly executed to the optimal progressive solution, and then the matching neural network is built to obtain the maximum segmentation threshold. The results show that the genetic neural network algorithm has a significant improvement in the comprehensive evaluation, recall rate, and accuracy of the three evaluation indexes, the average of which are 93.22%, 96.28%, 90.75%, respectively, to achieve the target of crack extraction under a complex background, and provide technical support for the later maintenance and maintenance of airport pavement.