To solve the problems of low prediction accuracy in aeroengine remaining useful life (RUL) prediction due to insufficient representative feature extraction, this study proposes an RUL prediction method based on multi-feature fusion for aeroengines. Exponential smoothing (ES) is performed to reduce the interference noise in the original data and thereby obtain relatively stable feature data. The time series features of the feature data are extracted by the bidirectional long short-term memory (Bi-LSTM) network and then assigned weights through the multi-head attention mechanism (Multi-attention). A convolutional long short-term memory (Conv-LSTM) network is designed to extract the spatio-temporal features of the feature data. Then, the handcrafted features of the feature data are extracted, and weights are calculated from the Softmax functions. A feature fusion framework is designed to fuse the above features, and RUL prediction is finally achieved by fully connected network regression. The commercial modular aero-propulsion system simulation (C-MAPSS) dataset is used to simulate and verify the proposed model. Compared with Bi-LSTM and other models, the proposed model achieves higher prediction accuracy and better adaptability.