Abstract: In order to accurately predict the changing trend of sensor data when the aeroengine is operating, and to effectively monitor the working status of the aeroengine, the sliding window algorithm is used to intercept the subsequences to construct the time series data set and standardize them based on the data of several main aeroengine sensors: engine high pressure compressor rotor speed (N₂), combustion chamber fuel nozzle pressure (P_tk), turbine temperature (T_t6) and so on. Then we propose a multi-sensor data prediction model of aeroengine based on Seq2Seq which is called AMSDPNN (aeroengine multi-sensor data prediction neural network) and optimizes this neural network model to realize the prediction of aeroengine multi-sensor data. The experimental results show that this model has better prediction results than other traditional data prediction models and the mean square error (MSE) is 0.1%. And the prediction of aeroengine sensor data is advanced by 320ms.