In　this　paper,　an　effective　wavelength　detection　approach　based　on　long　short-term　memory　(LSTM)　network　is　proposed　for　fiber　Bragg　grating　(FBG)　sensor　networks.　The　FBG　sensor　network　utilizes　a　model-sharing　mechanism,　where　the　whole　spectral　wavelength　is　divided　into　several　shareable　regions　and　spectral　overlap　is　allowed　in　each　region.　LSTM,　a　representative　recurrent　neural　network　in　deep　learning,　is　applied　to　learn　the　features　directly　from　the　spectra　of　FBGs　and　build　the　wavelength　detection　model.　By　feeding　the　spectra　sequentially　into　the　well-trained　model,　the　Bragg　wavelengths　of　FBGs　can　be　quickly　determined　under　overlap.　The　obtained　LSTM　model　can　be　repeatedly　used　without　re-training　to　improve　the　multiplexing　capability.　The　results　demonstrate　that　the　LSTM-based　method　can　realize　high-accuracy　and　high-speed　wavelength　detection　in　the　spectral　overlapping　situations.　The　proposed　approach　offers　a　flexible　tool　to　enhance　the　sensing　capacity　of　FBG　sensor　networks.　©　2019　Optical　Society　of　America　under　the　terms　of　the　OSA　Open　Access　Publishing　Agreement