Due　to　intensive　integration　and　seamless　continuous　operation,　the　overheated　artificially　intelligent　(AI)　integrated　circuit　systems　will　affect　the　operation　system＇s　effectiveness,　stability,　and　lifetime.　Therefore,　we　proposed　a　temperature　adaptability　memristor　in　the　silver　nanowires　(AgNWs)/nanocomposite/indium-tin-oxide　structure　in　this　study.　The　nanocomposite　is　the　nitrogen-doped　graphene/Ti3CNTx　MXene　blend　in　the　polyvinylidene　fluoride　matrix.　The　device　has　been　prepared　by　using　heterostructure　nanocomposites　with　a　low-cost　and　facile　all-solution　method.　The　device　mimicked　a　series　of　trained　behaviors　inherent　with　biological　synapses,　including　spike-timing-dependent　plasticity,　paired-pulse　facilitation,　short-term　potentiation/depression,　long-term　potentiation/depression,　and　excitatory　postsynaptic　currents.　In　addition,　the　device　demonstrated　significant　self-adaptability　to　temperature　due　to　the　involvement　of　the　homogeneously　distributed　conductive　heterostructure　and　the　filament　formation　ascribed　to　the　low　melting　point　of　AgNWs.　The　operation　of　the　device　shows　temperature　adaptability　owing　to　the　excellent　thermal　conductivity　and　small　thermal　expansion　coefficient　of　nitrogen-doped　graphene/Ti3CNTx.　This　finding　provides　viable　strategies　to　address　the　critical　challenges　of　deploying　AI　in　different　environments,　paving　the　way　for　the　development　of　more　efficient　and　resilient　neuromorphic　computing　systems.