The　fatigue　crack　growth　simulation　and　life　prediction　of　structures　are　implemented　in　this　paper　based　on　the　physics-informed　neural　networks　(PINNs).　Firstly,　the　enhanced　PINNs　are　proposed　by　introducing　the　crack　tip　asymptotic　displacement　fields,　so　that　the　crack　tip　stress　intensity　factors　can　be　calculated　accurately　even　when　the　number　of　collocation　points　is　small　and　the　distribution　grid　is　regular.　The　enhanced　PINNs　essentially　transform　the　solution　of　elastic　body　containing　crack　into　the　optimization　of　minimizing　the　constructed　loss　functions,　and　can　invert　the　fracture　parameters.　Then,　an　automatic　crack　propagation　simulation　method　is　developed　based　on　the　enhanced　PINNs.　The　network　architecture　and　the　overall　node　distribution　can　be　unchanged　during　the　crack　propagation　process,　and　only　new　crack　surfaces　need　to　be　processed　and　corresponding　loss　functions　need　to　be　modified.　Because　the　nodal　refinement　around　crack-tip　is　not　required,　this　simulation　method　is　convenient　and　can　accurately　predict　the　mixed-mode　crack　propagation　path.　Finally,　the　fatigue　crack　growth　life　algorithm　considering　overload　is　developed,　where　the　effect　of　each　overload　can　be　captured　by　the　cycle-by-cycle　method.　Based　on　this　algorithm,　the　retardation　behavior　can　be　characterized　and　the　fatigue　life　of　structure　under　the　load　spectrum　with　periodic　overloads　can　be　accurately　predicted.　The　sufficient　examples　are　given　to　verify　the　feasibility　and　accuracy　of　the　method　proposed　in　this　paper.　©　2024　Elsevier　Ltd