The　navigation　of　small　unmanned　aerial　vehicles　(UAVs)　mainly　depends　on　global　positioning　systems　(GPSs).　However,　GPSs　are　vulnerable　to　attack　by　spoofing,　which　causes　the　UAVs　to　lose　their　positioning　ability.　To　address　this　issue,　we　propose　a　deep　learning　method　to　detect　the　spoofing　of　GPS　signals　received　by　small　UAVs.　Firstly,　we　describe　the　GPS　signal　dataset　acquisition　and　preprocessing　methods;　these　include　the　hardware　system　of　the　UAV　and　the　jammer　used　in　the　experiment,　the　time　and　weather　conditions　of　the　data　collection,　the　use　of　Spearman　correlation　coefficients　for　preprocessing,　and　the　use　of　SVM-SMOTE　to　solve　the　spoofing　data　imbalance.　Next,　we　introduce　a　PCA-CNN-LSTM　model.　We　used　principal　component　analysis　(PCA)　of　the　model　to　extract　feature　information　related　to　spoofing　from　the　GPS　signal　dataset.　The　convolutional　neural　network　(CNN)　in　the　model　was　used　to　extract　local　features　in　the　GPS　signal　dataset,　and　long　short-term　memory　(LSTM)　was　used　as　a　posterior　module　of　the　CNN　for　further　processing　and　modeling.　To　minimize　randomness　and　chance　in　the　simulation　experiments,　we　used　the　10-fold　cross-validation　method　to　train　and　evaluate　the　computational　performance　of　our　spoofing　machine　learning　model.　We　conducted　a　series　of　experiments　in　a　numerical　simulation　environment　and　evaluated　the　proposed　model　against　the　most　advanced　traditional　machine　learning　and　deep　learning　models.　The　results　and　analysis　show　that　the　PCA-CNN-LSTM　neural　network　model　achieved　the　highest　accuracy　(0.9949).　This　paper　provides　a　theoretical　basis　and　technical　support　for　spoofing　detection　for　small-UAV　GPS　signals.