The　network　structure　within　polymers　significantly　influences　their　mechanical　properties,　including　their　strength,　toughness,　and　fatigue　resistance.　All-atom　molecular　dynamics　(AAMD)　simulations　offer　a　method　to　investigate　the　energy　dissipation　mechanism　within　polymers　during　deformation　and　fracture;　Such　an　approach　is,　however,　computationally　inefficient　when　used　to　analyze　polymers　with　complex　network　structures,　such　as　the　common　chemically　double-networked　hydrogels.　Alternatively,　coarse-grained　molecular　dynamics　(CGMD)　models,　which　reduce　the　computational　degrees　of　freedom　by　concentrating　a　set　of　adjacent　atoms　into　a　coarse-grained　bead,　can　be　employed.　In　CGMD　simulations,　a　coarse-grained　force　field　(CGFF)　is　a　critical　factor　affecting　the　simulation　accuracy.　In　this　paper,　we　proposed　a　data-based　method　for　predicting　the　CGFF　parameters　to　improve　the　simulation　efficiency　of　complex　cross-linked　network　in　polymers.　Here,　we　utilized　a　typical　chemically　double-networked　hydrogel　as　an　example.　An　artificial　neural　network　was　selected,　and　it　was　trained　with　the　tensile　stress-strain　data　from　the　CGMD　simulations　using　different　CGFF　parameters.　The　CGMD　simulations　using　the　predicted　CGFF　parameters　show　good　agreement　with　the　AAMD　simulations　and　are　almost　fifty　times　faster.　The　data-inspired　CGMD　model　presented　here　broadens　the　applicability　of　molecular　dynamics　simulations　to　cross-linked　polymers　and　has　the　potential　to　provide　insights　that　will　aid　the　design　of　polymers　with　desirable　mechanical　properties.　©　2024　2024　Author(s).