Feedback loops play pivotal roles in the regulation and control of many important cellular processes such as gene transcription, signal transduction, and metabolism. Hence, identification of feedback loops embedded in biomolecular regulatory networks is crucial to understanding the regulatory mechanisms underlying various cellular processes. In this paper, we introduce an identification method called the intermittent step perturbation method (ISPM) that can efficiently identify and locate feedback connectivities among reacting biomolecules. In particular, a sort of stochastic function called an intermittent step perturbation is applied to excite a given network. Then, we employ a statistical algorithm to analyze the resulting time-series data, thereby discerning any causal connection with a circular causal property. This circular causal property implies the existence of a feedback loop in the regulatory network. Finally, the proposed ISPM is demonstrated through an insulin signal transduction pathway model.