Laser　speckle　contrast　imaging　(LSCI)　can　be　applied　to　non-invasive　blood　perfusion　measurement　with　high　resolution　and　fast　speed.　However,　it　is　lack　of　measurement　accuracy.　The　aim　of　this　study　is　to　enable　quantitative　measurement　of　LSCI　by　using　Artificial　Intelligence　(AI),　and　this　is　achieved　by　using　a　set　of　experimental　data　obtained　from　a　rotating　diffuser　(a　tissue　phantom　mimicking　blood　flow　under　skins)　within　simulated　flow　velocity　of　0.08–10.74　mm/s.　These　data　were　used　to　train　a　three-dimensional　convolutional　neural　network　(3D-CNN)　to　establish　a　LSCI　velocities　prediction　model　(CNN-LSCI)　with　behavioral　feature　learning.　The　trained　model　has　0.33　MSE　(mean　squared　error)　and　0.34　MAPE　(mean　absolute　percentage　error)　and　is　verified　by　ten　phantom　velocities　(0.2-4　mm/s,　step　is　0.445　mm/s)　covering　the　typical　blood　flow　velocity　range　of　human　body　(0-2　mm/s)　with　the　correlation　of　0.98.　The　better　performance　of　the　proposed　model　is　demonstrated　by　the　results　compared　to　traditional　LSCI　and　multi-exposure　laser　speckle　contrast　imaging　(MELSCI).　This　study　shows　the　potential　of　LSCI　to　achieve　quantitative　blood　perfusion　measurement　using　machine　learning.　©　2023