The　fiber　grating　loop　ring-down　(FGLRD)　can　be　used　in　strain　sensors　to　reduce　costs　and　enhance　system　sensitivity.　Introducing　the　overlap　spectrum　demodulation　technology　in　the　FGLRD　can　improve　the　system＇s　linearity　and　expand　the　measurement　range.　However,　there　is　always　strong　amplified　spontaneous　emission　(ASE)　noise　owing　to　erbium-doped　fiber　amplifiers　(EDFAs)　in　the　FGLRD　structure.　The　noise　increases　the　difficulty　of　detecting　the　output　ring-down　spectrum　and　reduces　the　detection　accuracy.　This　study　proposes　a　machine　learning　algorithm　for　data　processing　of　chirped　FGLRD　strain　sensors　based　on　overlap　spectrum　demodulation.　It　uses　a　global　average　pooling　one-dimensional　convolutional　neural　network　algorithm　to　construct　a　data　recognition　model.　The　experimental　results　show　that　the　method　can　improve　the　recognition　accuracy　and　reduce　the　recognition　errors　caused　by　ASE　noise　compared　to　the　conventional　ring-down　spectral　peak　detection　method.　The　model　accuracy　was　100　%　for　the　training　and　test　sets　and　98　%　for　the　validation　set.　The　loss　value　was　close　to　0.06,　and　the　recognition　time　was　approximately　0.5　ms.