Cerebral stroke is an acute cerebrovascular disease characterized by high mortality and disability rates，imposing a significant burden on human society.Acupuncture，as a therapy that highlights the characteristics and advantages of traditional Chinese medicine(TCM)，is green，safe，and can promote neural plasticity.It has been widely used in the treatment of post-stroke neurorehabilitation with good clinical efficacy.However，the mechanism of acupuncture treatment for post-stroke recovery is not yet fully understood.Resting-state functional magnetic resonance imaging(rs-fMRI) is a non-invasive and safe imaging technique with high spatiotemporal resolution and has been widely used in the study of various neurological and psychiatric disorders.Through the application of this technique，the brain mechanisms underlying the effects of acupuncture have entered the visualized stage.With the gradual maturity of rs-fMRI technology，the data processing methods for rs-fMRI are also continuously evolving.The data processing methods for rs-fMRI study brain activity from two perspectives：functional segregation and functional integration.The former emphasizes the characteristics of individual rs-fMRI signals，such as local coherence and low-frequency amplitude.The latter investigates the interactions among multiple rs-fMRI time series signals，mainly including seed-based correlation analysis and independent component analysis.This article reviewed the research advances in the data processing methods of rs-fMRI for acupuncture treatment of stroke in recent years.