In　this　paper,　we　presented　three　neural　network　models　including　deep　neural　network　(DNN),　recurrent　neural　network　(RNN),　and　long　short-term　memory　neural　network　(LSTM),　which　are　proposed　to　predict　the　cohesive　zone　parameter　of　sintered　silver　DCB　joint　with　different　contents　of　nickel　modified　carbon　nanotube,　thus　reducing　the　complexity　of　CZM　parameter　acquisition　for　nanoparticle　reinforced　adhesive.　The　bilinear　CZM　model　is　used　as　the　prediction　target　model　for　sintered　silver　joints　with　different　contents　of　nickel-modified　carbon　nanotube　filler,　and　data　sets　suitable　for　different　networks　are　established　through　experimental　and　numerical　simulation　results.　Three　kinds　of　networks　are　trained　based　on　the　optimized　hyperparameters　obtained　from　the　Bayesian　hyperparameters　tuning　process.　The　results　show　that　DNN,　RNN,　and　LSTM　frameworks　can　all　predict　CZM　parameters　of　nanoparticle-reinforced　sintered　silver　adhesive　through　load-displacement　curves.　Based　on　loss　analysis　and　statistical　indicator　comparison　after　K-fold　cross-validation,　the　RNN　and　LSTM　models　have　better　prediction　accuracy　and　performance　than　the　DNN　model,　and　the　accuracy　of　the　LSTM　model　is　further　improved　compared　with　the　DNN　model.　RNN　and　LSTM　models　have　high　prediction　accuracy　and　stronger　recognition　ability　for　the　time　series　data,　they　can　be　used　as　suitable　alternative　models　for　inverse　recognition　of　CZM　parameters　of　nanoparticle-reinforced　adhesives,　and　have　broad　application　prospects.　©　2024　Elsevier　Ltd