US flight network,composed of 285airports(nodes)and 3 971flights(edges)is studied.A static network model and a dynamic network model of US flight network are established.Firstly,the characteristics of static network are analyzed.One finds that such a network is a″small-world″and″scale-free″network.The cumulative degree distributions of weighted network and unweighted network follow″Double Pareto Law″.And the degree exponent of weighted network is smaller than unweighted network.The average shortest-path length is 2.368 9,which is smaller than previous results.The clustering coefficient of unweighted network is 0.637 1and of weighted network is 0.653 6,which are both bigger than previous results.The correlation of degree,unweighted clustering coefficient and weighted clustering coefficient are also discussed.Secondly,the characteristics of dynamic network are studied.The structure of flight network is changing as the time goes by on a day.In rush hours,the network′s character of″scale-free″is stronger than other times.And then the relationships of topological structures and congestion effects are addressed. US flight network, composed of 285 airports (nodes) and 3 971flights (edges) is studied. A static network model and a dynamic network model of US flight network are established. Firstly, the characteristics of static network are analyzed. One finds that such a network is a “small-world” and “scale-free” network.The cumulative degree distributions of weighted network and unweighted network follow “Double Pareto Law” .And the degree exponent of weighted network is smaller than unweighted network.The average shortest- The path length is 2.368 9, which is smaller than previous results. The clustering coefficient of unweighted network is 0.637 1 and of weighted network is 0.653 6, which are both more than previous results. correlation of degree, unweighted clustering coefficient and weighted clustering coefficient are also discussed. Secondarily, the characteristics of dynamic network are studied. Structure of flight network is changing as the time goes by on a day. In rush hours, the network’s charac ter of “scale-free” is stronger than other times. And then the relationships of topological structures and congestion effects are addressed.