Aiming at the characteristics of complex degradation trend of aero-engine, this work presents a dilatated convolution network based on multi-feature attention model to predict the remaining useful life (RUL) of aero-engine. In this model，dilated convolution is used to enhance the ability to extract temporal features of sequence data and residual connections are established to improve the problem of gradient disappearance in traditional convolutional networks. Firstly, the raw input data are reconstructed by sliding time window of fixed length to intercept data along the time dimension. Then the dilated convolutional networks extract the temporal features of corresponding to each feature respectively. In the end, the feature attention mechanism is used to calculate the relative importance of features. Experimental results demonstrate that proposed algorithm has better accuracy of RUL estimation than the other comparative models.