The remaining life prediction of aero-engine was of great significance for its health management. Aiming at the long sequence and multiple dimensions aero-engine monitoring parameters, a Transformer model based on probabilistic sparse Self-Attention was proposed to realize the accurate prediction of the remaining life of the aero-engine. The regular Self-Attention mechanism in the original Transformer was replaced by ProbSparse Self-Attention, which made the model pay more attention to the important time nodes in the time series, greatly reduces the time dimension, and reduced the time and space complexity. Through the integrated information of the attention layer, the spatial features of the sensor were further extracted through the feedforward neural network layer and the convolutional layer. The encoding layer was connected by the dilated causal convolution, which expanded the receptive field and improves the model's ability to capture long sequence information. The algorithm was verified on the newly public N-CMAPSS dataset. The experimental results show that compared with the comparison models in the experiment, the RMSE and Score values of the proposed model are improved, and the inference speed is also better than other models.