Aiming at the problem that the setting of the complete failure threshold of rolling bearings is mostly selected according to artificial experience, and the degradation trajectory adaptation ignores the overall morphological trend change of the time series, a method for setting the failure threshold and predicting the remaining life of rolling bearings based on the consistent offset distance of morphological fluctuation is proposed. Firstly, the forward difference (FD) is introduced to preprocess the vibration signal, and the root mean square (RMS) value of the processed signal is calculated as the degradation indicator (DI). Secondly, the double exponential model is used to fit the DI curve to determine the total failure threshold (TFT) of the final reference bearing, so as to reduce the setting deviation of TFT. Finally, the similarity of the DI curve is calculated by using the morphology fluctuation conformance deviation distance (MFCDD) to complete the setting of the failure threshold of the test bearing, and the remaining useful life (RUL) prediction of the rolling bearing is completed by using the particle filter to update the double exponential model. The experimental results on the XJTY-SY dataset show that the score of rolling bearing RUL prediction is 82.97% and 73.64% higher than that of dynamic time warping matching method, convolutional neural network and bidirectional long short-term memory network prediction method, respectively. The experimental results on the PHM2012 dataset show that the score of rolling bearing RUL prediction is 99.99%, 60.65% and 99.90% higher than that of dynamic time warping matching method, convolutional neural network and bidirectional long-term and short-term memory network prediction method, long-term and short-term memory and self-attention mechanism prediction method.