The paper seeks to overcome the limitations inherent in traditional transition network methods, which primarily concentrate on transition frequencies between adjacent symbols, neglecting broader transition relationships. We present a novel approach called “multi-span transition network.” This method excels at capturing dynamic information within time series by incorporating transitions across higher time-scale patterns. We also propose a conditional entropy measure to assess the complexity of time-series data derived from the multi-span transition network. With expanding dimensionality, the multi-span transition network adeptly discriminates between various types of time series and unveils concealed information. The conditional entropy of the multi-span transition network exhibits a robust correlation with the maximum Lyapunov exponent of the system. The conditional entropy of a multi-span network can distinguish the time series of different states and determine chaos degradation. Employing the multi-span transition network for the classification of epileptic EEG data resulted in a substantial enhancement in accuracy compared to conventional transition network methods. The method is a more general form of the traditional transition network and is more generalizable.