Aiming at the problems for which the first predicting time (FPT) of bearing remaining useful life (RUL) is based on subjective selection and maintenance risks caused by predictive lag. A stochastic configuration networks (SCNs)-based bearing residual life prediction method is proposed. Firstly, the complementary ensemble empirical mode decomposition (CEEMD) is used to decompose the original bearing horizontal vibration signal, then extract its time-domain and frequency-domain signals to construct fusion features. Secondly, the health state is divided by wavelet clustering to find the appropriate FPT, and the health data set is constructed by combining the characteristics of the energy response bearing degradation. The prediction is made by SCNs network offline modeling, and the prediction results are corrected according to the slope of the fitted curve and the RMSE index. Through experimental analysis, the comprehensive score of the proposed method is as high as 0.83, and the mean absolute deviation (MAD) and standard deviation (SD) of the error percentage are 5.26 and 3.38. Compared with other prediction methods, the proposed method has higher prediction accuracy.