Structural　health　monitoring　(SHM)　plays　a　vital　role　in　promptly　identifying　structural　damage　in　aircraft,　optimizing　maintenance,　and　reducing　costs;　however,　it　faces　significant　challenges　in　practical　applications,　in　that　it　mainly　needs　to　process　a　large　number　of　continuously　collected　sensor　data,　which　are　inevitably　contaminated　by　random　noise.　This　study,　therefore,　maps　the　relationship　between　Lamb　wave　signal　data　with　noise　and　the　health　condition　of　aircraft　structures　using　an　end-to-end　approach　to　construct　a　deep　learning　(DL)　framework.　The　framework　integrates　deep　residual　convolutional　networks　(DRSNs)　for　feature　extraction,　efficient　channel　attention　(ECA)　for　feature　enhancement,　and　long　short-term　memory　(LSTM)　in　analyzing　time　series　data.　Lamb　wave　signal　datasets　considering　different　damage　locations　and　severity　are　obtained　by　lead　zirconate　titanate　(PZT)　sensors　on　the　aircraft　structure,　and　the　datasets　are　destroyed　by　using　multiple　levels　of　Gaussian　random　noise　to　approximate　the　noise　disturbances　and　unavoidable　unpredictability　of　the　industrial　environment.　The　experimental　data　confirms　that　the　performance　metrics　of　the　proposed　framework　for　damage　presence,　localization,　and　quantification　tasks　are　all　above　97%　in　a　noise-free　environment.　When　dealing　with　high-noise　scenes,　the　framework　provides　stronger　antinoise　robustness　and　higher　accuracy　compared　to　existing　state-of-the-art　(SOTA)　methods.　Quantitative　analysis　of　ablation　experiments　and　visualization　of　the　t-distributed　stochastic　neighbor　embedding　(t-SNE)　algorithm　is　applied　to　reveal　the　contribution　of　each　component　of　the　designed　framework　toward　feature　extraction　and　damage　identification　of　Lamb　wave　signals　in　noisy　environments.