Diffuse　correlation　spectroscopy　(DCS)　is　a　noninvasive　technique　that　derives　blood　flow　information　from　measurements　of　the　temporal　intensity　fluctuations　of　multiply　scattered　light.　Blood　flow　index　(BFI)　and　especially　its　variation　was　demonstrated　to　be　approximately　proportional　to　absolute　blood　flow.　We　investigated　and　assessed　the　utility　of　a　long　short-term　memory　(LSTM)　architecture　for　quantification　of　BFI　in　DCS.　Phantom　and　in　vivo　experiments　were　established　to　measure　normalized　intensity　autocorrelation　function　data.　Improved　accuracy　and　faster　computational　time　were　gained　by　the　proposed　LSTM　architecture.　The　results　support　the　notion　of　using　proposed　LSTM　architecture　for　quantification　of　BFI　in　DCS.　This　approach　would　be　especially　useful　for　continuous　real-time　monitoring　of　blood　flow.　(c)　2021　Optical　Society　of　America　under　the　terms　of　the　OSA　Open　Access　Publishing　Agreement