One of the core technologies in predictive maintenance is equipment life prediction. The aeroengine serves as the main power source for aircraft, and its reliability is crucial for ensuring safe flight missions. Using equipment life prediction can better manage the use and replacement of aircraft engines, which plays a very important role in the area of improving their availability and enhancing aircraft safety. To better predict the remaining service life of aircraft engines under various working conditions, this study proposes an aeroengine life prediction artificial intelligent model based on a series of Convolutional Neural Network (CNN) as well as the Bidirectional Long-Short-Term Memory network (BiLSTM). The attention mechanism is arranged into the designed network to enhance the feature weight of the data that need to pay attention to, thereby improving the prediction accuracy. Experiments were conducted on the NASA C-MAPSS simulation dataset. In addition, results of experiments are compared at the same time with several traditional methods. The contrast of the different results demonstrate that the model is able to predict the remaining life of aeroengines with higher accuracy than traditional methods.