Rolling bearing is an important component of mechanical transmission equipment. The prediction of its performance degradation trend is the key to ensure the safe and stable operation of the equipment. In order to improve the accuracy of rolling bearing performance degradation trend prediction, a rolling bearing performance degradation trend prediction method based on the combination of multi-head-attention (MHA) and long short-term memory (LSTM) is proposed. Firstly, the multi domain features of time domain, frequency domain, time-frequency domain and Weibull parameters are constructed, and the multi domain features are screened according to the comprehensive performance degradation index. Secondly, the attention mechanism is used to enhance the weight of key features, PCA is used for feature fusion, and LSTM model is further used to predict the performance degradation trend of rolling bearing. Finally, the method proposed in this paper is verified by using the bearing fatigue life experimental data of NSF I / UCR center, and compared with several other models, which shows that the method proposed in this paper can more accurately predict the performance degradation trend of rolling bearing.