Abstract: Based on CNN+LSTM hybrid neural network, a fault time series forecasting model was established for the flap mechanism of automatic gate machine for metro. And then a case analysis was presented based on this model by using the fault data of the flap mechanism of a certain type of automatic gate machine. By comparing this hybrid neural network model with other three single forecasting models that are ARIMA, CNN and LSTM, the results show that the accuracy of forecasting by this CNN+LSTM hybrid neural network model is higher and it has a good application prospect. The research results can be used to support the formulation and optimization of the maintenance plan for the flap mechanism of automatic gate machine.