<p>Abrupt transitions that are associated with global crisis events can lead to systemic collapse. Previous models for investigating early warning signals for time series–based oil systems focused on the fluctuation characteristics of single points and ignored the dynamic process in the oil series, which is characterized both by historical changes in the oil series and by its nonlinear fluctuations coupled with related variables. In this study, an early warning model that combines a deep learning model, a Markov regime-switching model and a reconstructed hybrid network, including a self-dynamic network and a relationship-dynamic network, is proposed. West Texas Intermediate (WTI) crude oil and natural gas futures daily prices are selected as the sample data. Abrupt transitions are identified, and their characteristics that are influenced by typical events are investigated. The dynamic features can be measured through the structures of the hybrid network; the in-degrees, out-degrees, and weighted degrees of the nodes follow power-law distributions. Early warning signals for abrupt transitions can be effectively captured via the deep learning model. Importantly, the deep learning-based early warning model integrated with the hybrid network outperforms that integrated with the self-dynamic network, with the average accuracies of both models on the training and testing sets exceeding 90%. This work contributes to energy management research and provides early warning tools for policymakers and market investors.</p>