The　inverse　identification　of　cohesive　zone　parameters　is　an　important　topic　in　the　field　of　fracture　mechanics.　In　this　paper,　three　data-driven　models,　including　multilayer　perceptron　(MLP),　convolutional　neural　network　(CNN),　and　dynamic　convolutional　neural　network　(DCNN),　were　presented　to　predict　the　cohesive　zone　parameters　of　sintered　nano-silver　end　notched　flexure　(ENF)　joints.　Based　on　the　construction　of　experimental　and　numerical　datasets　of　load　versus　displacement　curves,　bilinear　cohesive　zone　model　(CZM)　parameters　of　sintered　silver　joints　are　adopted　as　the　prediction　target.　The　investigation　shows　that　MLP,　CNN,　and　DCNN　are　all　valid　for　predicting　CZM　parameters　through　load　versus　displacement　curves　with　reasonable　accuracy.　However,　DCNN　has　better　prediction　accuracy　and　performance　than　those　of　CNN　and　MLP　models　based　on　loss　analysis,　statistical　indicator　comparison,　and　K-fold　cross　validation.　DCNN　can　be　adopted　as　the　suitable　surrogate　model　for　CZM　parameters　inverse　identification　with　high　prediction　accuracy.　Otherwise,　DCNN　is　also　not　sensitive　to　the　load　versus　displacement　curves　data　length.　However,　the　computation　efficiency　of　DCNN　during　the　training　process　is　not　as　high　as　that　of　MLP.　Those　three　methods　presented　in　this　paper　are　very　hopeful　to　be　adopted　for　other　inverse　identifications　of　CZM　parameters　for　various　kinds　of　adhesive　joints.