Abstract:In order to accurately grasp the changing law of air traffic flow and the inherent characteristics of the air traffic system, it is necessary to analyze the time series of air traffic flow based on fractal characteristics of complex networks. The air traffic flow data was collected, a complex network model was constructed using the visual graph method, and the network topology was analyzed. It was verified that the degree distribution of the network obeyed a power-law distribution, and the slope of the fitted straight line was -2.086, which proved that the network is a scale-free network with a single Fractal features. It is verified that the minimum number of boxes required to cover all nodes in the entire network has a power-law relationship with the diameter of the box. The slope of the fitted line is -0.2121, and the correlation coefficient is -0.8722, which again proves that the network has monofractal characteristics. By verifying that the image of the network generalized fractal dimension with respect to the parameters is nonlinear, the slopes of the fitting lines are -1.942, -1.936, and -1.78, respectively, and the correlation coefficients are all above 0.8. The fitting effect is good, which proves that the network has multifractal characteristics. . By calculating the similarity of the power exponents of the network before and after renormalization, it is proved that the network has self-similarity. The results show that it is feasible and effective to apply the theory of complex network to analyze the time series of air traffic flow, which lays a foundation for further in-depth application research.