Aiming at the problem that modern industrial systems tend to focus on their predictive performance while paying little attention to equipment maintenance decision-making, a data-driven dynamic predictive maintenance method is proposed to avoid sudden system failures and ensure safe operation. First, the health status of the turbofan engine is monitored in real-time to obtain operating data, which is used to establish a turbofan engine remaining useful life model based on a convolutional neural networks-bidirectional gated recurrent unit-attention mechanism. The hyperparameters of the CNN-BiGRU-A are optimized using the black hawk optimization algorithm; second, the monitored data is input into the trained integrated network, and a dynamic predictive maintenance strategy with uncertain system task cycle is proposed based on the predicted remaining useful life; finally, the proposed method is verified by using the C-MAPSS data set to show that it can improve equipment predictive performance and perform good predictive maintenance afterward.