AbstractIn this paper, we analyzed time series of Shanghai stock index with complex network theory. The degree distribution of the network extracted from the original series can be well fitted with a power law, while the network from return series is governed by an exponential degree distribution. Compared with the time series of standard Brownian motion, we found that the dynamics of the original series can be identified, but the return series has the similar topology with a random one. Moreover, in the scale-free networks from original series, the small-world property is detected and the time interval distribution between connected pairs decays as an exponential function, which implies that nodes correlated with a given one appear in a Poisson process.