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Abostr: As an important equipment for metallurgy, the rolling mill has complex working conditions, and its key parts are prone to failure, so it is difficult to make early fault diagnosis and trend prediction. Taking bearing as an example, a new performance degradation index is proposed to detect the moment of early failure. To prevent mill work environment complex problems, first of all samples were collected for signal de-noising, implementation, to eliminate the noise signal after the cross-correlation function is used to analyse the data before and after the sample cross-correlation analysis, then analyze the data from all of the extreme value point energy and total energy ratio, ratio into the information entropy formula, finally will do for ultimate performance degradation index, namely It is the cross correlation energy ratio entropy, and the validity of the index is verified by envelope spectrum analysis. Aiming at the problem of bearing performance degradation trend prediction, BiGRU-GRU network is established based on the respective advantages of Gate Recurrent Unit (GRU) and Bidirectional Gate Recurrent Unit (BiGRU). The collected data are divided into training data and test data. After training in the training data, the test data are predicted to realize the prediction of bearing performance degradation trend. Comparative experiments show that the proposed performance evaluation index and network have better effects than the general index and network.