Constrained　by　the　substantial　computational　time　required　for　numerical　simulation,　a　deep　learning　technique　is　applied　to　investigate　fluid　flow　and　heat　transfer　processes　in　metal　foam　with　a　hierarchical　pore　structure.　This　work　adopted　a　3D　convolutional　neural　network　(CNN)　combining　U-Net　architecture　to　predict　velocity　and　temperature　distributions,　alongside　corresponding　permeability　and　overall　heat　transfer　coefficient.　This　approach　demonstrates　excellent　capability　in　intricate　image　segmentation.　The　training　sets　were　acquired　by　lattice　Boltzmann　method　(LBM)　simulations.　The　CNN　model,　trained　on　a　substantial　amount　of　data,　demonstrates　remarkable　precision,　exhibiting　mean　relative　errors　of　0.57%　for　permeability　prediction　and　2.27%　for　overall　heat　transfer　coefficient　prediction.　Moreover,　in　CNN　prediction,　a　broader　range　of　structure　parameters　and　boundary　conditions　beyond　those　in　the　training　set　was　used　to　evaluate　the　practicability　of　the　trained　CNN　model.　In　contrast　to　numerical　simulation,　the　CNN　model　economizes　approximately　95.41%　and　99.57%　of　computational　time　for　velocity　and　temperature　distribution　prediction,　respectively,　providing　a　novel　approach　for　exploring　transport　processes　in　metal　foam　with　hierarchical　pore　structure.　©　2024　Institute　of　Process　Engineering,　Chinese　Academy　of　Sciences.