In order to accurately analyze the dynamics and changing trends of KPI data in the daily monitoring of cloud computing clusters and predict the subsequent development to achieve the goal of improving the high availability of cloud computing clusters, this paper proposes a three-frequency cloud KPI data prediction based on the combined attention model EAAT method. First, low, medium and high frequency intrinsic mode variables (IMFs) of cloud KPI data are obtained based on Empirical wavelet transform (EWT) to reduce the complexity of data prediction. Secondly, according to the information characteristics of low, medium and high frequency IMFs obtained from the decomposition, the ARIMA, Autoformer, and TPA-BiLSTM models are used to predict each type of IMFs. Finally, the classification prediction results are combined through the inverse transformation IEWT to obtain the prediction result of the KPI. The prediction method in this paper has been verified on four data sets from Google and Amazon. Regardless of whether the data is periodic or not, the prediction method in this paper has good results. The root mean square error of the EWT-IF-LSTM model on the Google data set is reduced by about 11.26%.