In order to accurately predict rolling bearing degradation trend, a combined prediction method of KPCA and TCN-Attention was proposed. Firstly, KPCA was used to extract nonlinear features from the high-dimensional feature sets of bearings, and the first principal component was used as the performance degradation index of bearings to normalize and smooth the first principal component. then, an attention mechanism is added to the temporal convolutional network TCN, the weight coefficients of the key features of the hidden layer are given, the part that contributes the most to the local features extracted by the TCN at each time step is found, and then the key information is extracted; finally, the Cincinnati IMS is used. The life cycle data of the bearing outer and inner rings verify the feasibility of the method. The experimental results show that compared with TCN, Gru and LSTM without attention mechanism, the predicted values of RMSE and MAE of the outer loop are reduced to 0.00299 and 0.00217, respectively, and the predicted values of RMSE and MAE of the inner loop are reduced to 0.03401 and 0.02490, respectively , with higher prediction accuracy.