In　this　paper,　we　proposed　a　distortion-tolerant　method　for　FBG　sensor　networks　based　on　Estimation　of　Distribution　Algorithm　(EDA)　and　Convolutional　Neural　Network　(CNN).　Addressing　the　parameter　reconstruction　of　reflection　spectrum,　an　objective　function　is　formulated　to　pinpoint　the　Bragg　wavelength　detection　problem,　with　the　optimal　solution　acquired　via　EDA.　By　incorporating　spectral　distortion　into　the　objective　function,　the　EDA-based　method　effectively　manages　distorted　spectrums,　ensuring　the　fidelity　of　wavelength　data.　Further,　CNN　aids　in　extracting　features　from　the　entire　FBG　sensor　network’s　wavelength　information,　facilitating　the　creation　of　the　localization　model.　By　sending　the　reliable　wavelength　data　obtained　by　EDA　to　the　trained　model,　swift　identification　of　the　load　position　is　achieved.　Testing　revealed　that　under　conditions　of　spectral　distortion,　EDA　can　adeptly　detect　the　Bragg　wavelength.　Additionally,　the　CNN-trained　localization　model　outperforms　other　machine-learning　techniques.　Notably,　experimental　results　demonstrate　that　the　proposed　EDA　surpasses　the　second-ranked　method,　i.e.,　the　Maximum　method,　achieving　a　Root　Mean　Square　Error　(RMSE)　of　merely　1.4503mm　which　is　substantially　lower　than　the　6.2463mm　achieved　by　the　Maximum　method.　The　average　localization　error　remains　under　2mm　when　5　out　of　9　FBGs’　reflection　spectra　are　distorted.　Furthermore,　Bragg　wavelength　detection　error　stays　below　1pm　amid　spectral　distortion.　Consequently,　our　method　offers　promising　application　prospects　for　long-term　FBG　sensor　network　monitoring,　ensuring　high　accuracy　and　robustness　in　detecting　structural　damage.　IEEE