Aiming at the problems of oil monitoring data of wet clutch, such as scattered sources, a large amount of data and unstable time axis, the multi-data obtained from the spectral analysis are fused. Using the advantages of real-time prediction and accurate prediction of the Wiener process, a model is established to predict the remaining life of the clutch. Firstly, the indicator elements obtained from the clutch life test are fused by permutation entropy weighted evidence fusion method to construct the health index; Secondly, combined with the Wiener process, the degradation model is established, and the parameters in the model are estimated by the maximum likelihood method; Thirdly, the residual life prediction model of the clutch is obtained by updating the parameters according to the historical degradation data; Finally, by comparing the prediction model with an example, it is found that the prediction accuracy of the Wiener process prediction model based on the health index of multi-element fusion is greatly improved compared with the single indicator element prediction. Its prediction point is closer to the experimental value. Through observation, it is also found that when the wet clutch operates for about 50 to 60 h, the prediction point has an obvious change, while the prediction point has an obvious deviation from 220 to 230 h, and it is close to the test value again at about 240 h. The mutation point corresponds to the three stages of clutch wear: initial wear, normal wear and severe wear. The research results show that the prediction model based on oil spectrum data and the Wiener process has the advantages of strong real-time prediction and high prediction accuracy for the remaining life of the wet clutch. The comparison between the prediction results and the test values shows that the different wear states of wet clutch also have a certain impact on the prediction results, especially the wear state transition point has a greater impact on the prediction results.