The data-driven approach to rolling bearing remaining useful life (RUL) prediction shows great potential, but there is still room for improvement. Therefore, a prediction method of rolling bearing RUL based on Autoformer model is proposed. Combined with the expert knowledge in this field, the original signal of rolling bearing is artificially extracted and optimized, and the complex mapping relationship between input features and RUL is mined by using the powerful multi-dimensional feature extraction capability of Transformer models. According to the periodic characteristics of the vibration signal of rolling bearings, the Autoformer model is used to decompose the time series to deal with the trend term and the periodic term separately. Experimental results show that the average scores of the proposed prediction method on the PHM2012 dataset is improved by 50.03%, 21.31% and 19.93% respectively, compared with other methods in the literature. Proves the superiority of this method.