Continuous　monitoring　of　cerebral　blood　flow　(CBF)　provides　crucial　information　for　clinical　diagnosis　and　treatment　of　various　cerebral　diseases.　Diffuse　correlation　spectroscopy　(DCS)　uses　near-infrared　(NIR)　coherent　point-source　illumination　to　accommodate　spectroscopic　measurements　of　CBF　variations.　In　this　paper,　we　investigate　and　evaluate　a　deep　learning　method　for　CBF　quantification　based　on　proposed　ConvGRU　model.　Two　in　vivo　experiments,　i.e.,　deep-breath　experiment　and　breath-holding　experiment,　were　established　to　measure　normalized　intensity　autocorrelation　function　data.　Compared　to　conventional　methods,　promising　results　for　assessing　changes　of　CBF　were　gained　by　the　developed　ConvGRU　models.　Our　results　suggest　that　ConvGRU-based　deep　learning　method　can　provide　an　alternative　method　for　continuous　monitoring　of　CBF.