In order to improve the high accuracy and reliability of drone pitch angle fault data processing and prediction, and avoid increasing the cost of drone flight testing, long short term memory (LSTM), attention mechanism + LSTM model, and autoregressive integrated moving average model (ARIMA) model were used to predict pitch angle faults data in drone systems. The results show that for ARIMA, mean absolute error (MAE) is 0.35, root mean square error (RMSE) is 0.73, mean absolute percentage error (MAPE) is 23.80%. For LSTM model, MAE = 0.49, RMSE = 0.74, MAPE = 45.20%. For attention mechanism + LSTM model, MAE = 0.17, RMSE = 0.53, MAPE = 18.93%. It can be seen that the attention mechanism + LSTM model is more suitable for predicting the pitch angle of flight tests data than the other two models. The above three methods have practical significance for predicting unmanned aerial vehicle faults. Effective prediction can promote the latest progress in anomaly detection of autonomous aircraft and mobile robots or foreign direct investment research, in order to further improve the safety of automatic and remote flight operations.