In order to solve the problem of low prediction accuracy of RUL by traditional forecasting methods, a new forecasting method of turbofan engine life based on Kalman filter and time convolutional neural network (KF-TCN) was proposed. Firstly, in order to reduce the noise interference of the sensor when collecting data, Kalman filter algorithm was used to filter the extracted features. Secondly, the KF-TCN model was constructed, and the filtered data was input into the time convolutional network to capture the time dependence of the time series data and output the RUL predicted value. Finally, taking FD001 subset as an example, commercial modular aero-propulsion system simulation (C-MAPSS) aero-engine data set was used to verify the model. The results show that compared with the RUL prediction results of TCN, gated recurrent unit (GRU) and long short-term memory (LSTM) models, the prediction error of KF-TCN model constructed is smaller in the process of engine RUL prediction, which provides a way for engine life prediction.