Current existing literatures can characterize the long-term fluctuation of crude oil price time series, however, it is difficult to detect the fluctuation mechanism specifically under short term. Because each fluctuation pattern for one short period contained in a long-term crude oil price time series have dynamic characteristics of diversity; in other words, there exhibit various fluctuation patterns in different short periods and transmit to each other, which reflects the reputedly complicate and chaotic oil market. Thus, we proposed an incorporated method to detect the fluctuation mechanism, which is the evolution of the different fluctuation patterns over time front the complex network perspective. We divided crude oil price time series into segments using sliding time windows, and defined autoregressive Modes based on regression models to indicate the fluctuation patterns of each segment. Hence, the transmissions between different types of autoregressive modes Over time form a transmission network that contains rich dynamic information. We then capture transmission characteristics of autoregressive modes under different periodic time series through the structure features of the transmission networks. The results indicate that there are various autoregressive modes with significantly different statistical characteristics under different periodic time series. However, only a few types of autoregressive modes and transmission patterns play a major role in the fluctuation mechanism of the crude oil price, and these key autoregressive modes have specific transmission targets. Thus, it is possible to predict the most probable transmission mode from the former mode to a latter one based on the distribution of the transmission probabilities. Moreover, some autoregressive modes often appear together in a certain period and thus form a cluster during the transmission process. All autoregressive modes could be categorized into several clusters, and each cluster then has its own preference to transmit into other clusters. This work not only proposes a distinctive perspective for analyzing the fluctuation mechanism of crude oil price time series, but also provides valuable information regarding different periodic time series for decision makers. (C) 2017 Elsevier Ltd. All rights reserved.