To address the problem that unmanned aerial vehicle (UAV) is vulnerable to GPS spoofing, an UAV GPS spoofing detection model based on long short-term memory ( LSTM) is proposed. In order to improve the training accuracy of the model, the training dataset was firstly enhanced using time series generative adversarial networks (TimeGAN) to compensate for the lack of training data and to compare the performance difference between the enhanced dataset and the original dataset. The LSTM model was then built, and experimental results show that the accuracy, precision, recall and F1 value trained by the TimeGAN+LSTM model under simulation experiments are 98. 08%, 98. 55%, 98. 07% and 98. 31%. Finally, the comparison with the traditional machine learning model proves that the proposed spoofing detection model has better performance metrics. The model can achieve effective detection of UAV GPS spoofing signals.