Aimed at the problems that interpretable ability of composite health indicators constructed by deep learning is poor, and the prediction results are difficult to quantify the uncertainty of engine remaining life, an aero-engine remaining life prediction method based on data fusion and gate recurrent unit (GRU) is proposed. Firstly, the one-dimensional composite health index is constructed by weighted fusion of multi-source sensor data. And then, the Bootstrap method is utilized for putting the samples back on the one-dimensional composite health index，obtaining the engine degradation characteristic samples of n groups. Finally, “n+1”remaining life prediction models based on GRU are constructed by using one-dimensional composite health indicators and the n-groups of engine degradation characteristic samples, the prediction interval of engine remaining life is quantified. In order to prove the feasibility and superiority of the proposed method, the turbofan engine degradation dataset (C-MAPSS) is used for the experiment, and the root mean square error obtained is 15.825 4, the score function value is 344.210 5. The results show that this method can not only achieve the better prediction results, but also can effectively solve the defects of deep learning in engine remaining life prediction.