The　human　brain　possesses　a　remarkable　ability　to　memorize　information　with　the　assistance　of　a　specific　external　environment.　Therefore,　mimicking　the　human　brain＇s　environment-enhanced　learning　abilities　in　artificial　electronic　devices　is　essential　but　remains　a　considerable　challenge.　Here,　a　network　of　Ag　nanowires　with　a　moisture-enhanced　learning　ability,　which　can　mimic　long-term　potentiation　(LTP)　synaptic　plasticity　at　an　ultralow　operating　voltage　as　low　as　0.01　V,　is　presented.　To　realize　a　moisture-enhanced　learning　ability　and　to　adjust　the　aggregations　of　Ag　ions,　we　introduced　a　thin　polyvinylpyrrolidone　(PVP)　coating　layer　with　moisture-sensitive　properties　to　the　surfaces　of　the　Ag　nanowires　of　Ag　ions.　That　Ag　nanowire　network　was　shown　to　exhibit,　in　response　to　the　humidity　of　its　operating　environment,　different　learning　speeds　during　the　LTP　process.　In　high-humidity　environments,　the　synaptic　plasticity　was　significantly　strengthened　with　a　higher　learning　speed　compared　with　that　in　relatively　low-humidity　environments.　Based　on　experimental　and　simulation　results,　we　attribute　this　enhancement　to　the　higher　electric　mobility　of　the　Ag　ions　in　the　water-absorbed　PVP　layer.　Finally,　we　demonstrated　by　simulation　that　the　moisture-enhanced　synaptic　plasticity　enabled　the　device　to　adjust　connection　weights　and　delivery　modes　based　on　various　input　patterns.　The　recognition　rate　of　a　handwritten　data　set　reached　94.5%　with　fewer　epochs　in　a　high-humidity　environment.　This　work　shows　the　feasibility　of　building　our　electronic　device　to　achieve　artificial　adaptive　learning　abilities.