Growing　incidences　of　cardiovascular　diseases　have　driven　demand　for　continuous　tissue　oxygen　metabolism　monitoring.　Diffuse　optical　spectroscopy　(DOS)　or　near-infrared　spectroscopy　(NIRS)　is　typically　employed　to　measure　oxygenated　and　de-oxygenated　hemoglobin,　while　diffuse　correlation　spectroscopy　(DCS)　provides　a　direct　measure　of　tissue　blood　flow　noninvasively.　Traditionally,　combined　DCS　and　DOS/NIRS　approaches　are　utilized　for　monitoring　oxygen　metabolism,　which　needs　two　different　numerical　models　to　calculate　blood　flow　and　oxygen　saturation.　In　this　paper,　we　evaluate　the　utility　of　multi-wavelength　diffuse　correlation　spectroscopy　(mwDCS)　using　a　proposed　deep　learning　approach,　i.e.,　long　short-term　memory　(LSTM)　based　recurrent　neural　network　(RNN).　Arterial　occlusion　experiments　were　performed　in　vivo　to　generate　the　data　from　6　healthy　individuals.　Further,　based　on　the　dataset　acquired　by　mwDCS,　a　LSTM-based　RNN　model　was　developed　to　directly　assess　changes　of　blood　flow　and　oxygen　saturation.　Compared　to　conventional　methods,　the　in　vivo　experiments　in　this　study　demonstrated　a　more　portable　system　with　promising　results.　Pearsons　correlation　coefficients　of　the　proposed　model　reached　0.82　and　0.64　for　the　changes　of　blood　flow　and　oxygen　saturation,　respectively.　Our　results　shows　that　mwDCS　using　LSTM-based　RNN　model　provides　an　alternative　method　for　continuous　tissue　oxygen　metabolism　monitoring.