A　long　short-term　memory　kolmogorov-arnold　network　(LSTM-KAN)　computational　model　that　can　accurately　estimate　the　loads　of　an　aircraft　landing　gear　is　proposed,　which　can　accurately　estimate　the　loads　based　on　monitoring　the　strain　distribution　of　the　landing　gear　structure.　First,　the　ground　strain　load　test　system　based　on　the　landing　gear　structure　is　built.　Fiber-optic　grating　strain　sensors　were　installed　at　the　monitoring　positions　of　the　landing　gear,　and　the　strain　data　under　various　loading　conditions　were　collected　using　the　loading　system.　Experimental　datasets　for　model　training　and　testing　were　generated　based　on　the　obtained　strain　and　load　data,　the　strain-load　calculation　model　was　trained　and　tested,　and　its　prediction　results　were　compared　with　the　traditional　linear　regression　method　and　other　algorithms　using　the　same　experimental　datasets.　The　load　prediction　results　show　that　the　maximum　absolute　percentage　errors　of　the　loads　in　the　three　directions　are　2.74　%,　2.41　%　and　3.29　%,　respectively,　and　the　corresponding　root-mean-square　errors　are　51.31　N,　202.29　N　and　39.86　N,　respectively,　and　the　overall　average　absolute　percentage　errors　are　reduced　to　less　than　1　%,　and　the　three-direction　ones　are　0.81　%,　0.73　%　and　0.88　%,　respectively,　which　proves　that　the　LSTM-KAN　model　has　better　performance　than　the　multiple　linear　regression　method　and　other　neural　network　algorithms,　and　can　be　effectively　and　accurately　predicted　in　the　field　of　health　monitoring　of　aircraft　landing　gears　and　other　aircraft　structures.