In　this　paper,　a　compact　and　lightweight　high-precision　airborne　fiber-optic　strain　sensor　is　designed,　and　a　strain-load　prediction　model　based　on　convolutional　neural　network　and　long　short-term　memory　network　(ConvLSTM)　is　proposed　to　validate　the　collected　landing　gear　strain　data.　Firstly,　based　on　the　fiber　Bragg　grating　(FBG)　sensing　principle　and　simulation　validation,　small　and　lightweight　strain　and　temperature　sensors　were　fabricated,　and　key　performance　parameters　such　as　sensitivity　and　linearity　were　comprehensively　investigated,　and　they　were　mounted　on　the　left　landing　gear　of　the　aircraft　to　conduct　strain　monitoring　by　loading　a　three-way　load,　and　the　ConvLSTM　model　was　used　to　train　and　test　the　strain-load　mapping　with　the　maximum　relative　error,　average　relative　error　and　variance　as　indicators　to　evaluate　its　prediction　accuracy　and　stability.　The　experimental　results　show　that　the　strain　measurement　accuracy　is　maintained　within　2.5　%　and　the　strain　sensitivity　is　as　high　as　1.3　pm/mu　8　within　the　+/-　5000　mu　8　measurement　range　of　the　strain　sensor;　the　maximum　relative　errors　of　the　X,　Y,　and　Z　load　predictions　are　6.03　%,　3.75　%,　and　4.12　%,　respectively,　and　the　overall　average　relative　errors　are　2.38　%,　0.27　%,　and　0.76　%,　with　variances　of　0.23　N,　0.61　N,　and　0.61　N,　respectively.　0.23　N,　0.61　N　and　0.12　N,　indicating　that　the　model　predictions　are　stable　and　highly　accurate,　demonstrating　higher　prediction　accuracy　when　compared　with　traditional　multiple　linear　regression　methods.　The　results　of　this　research　have　important　application　value　in　the　field　of　aircraft　structural　health　monitoring.