Aiming at the problem of multi time-step-length prediction of lifting machinery, a method for health prediction of lifting machinery equipment combining convolutional neural network (CNN) and bidirectional long short-term memory with encoder-decoder(ED-BLSTM) is proposed. Firstly, sorting the monitoring data in chronological order, and then segmenting and reorganizing the data set when ensuring the same input and outputting time step size. Then inputting the processed data set to the convolutional neural network, and extracting its main features to obtain Multi-dimensional matrix. This matrix is used as input to train the bidirectional long and short term cyclic neural network based on the encoder-decoder, and establishing the target prediction model of multi-time unit step of lifting machinery. Comparative experiments show that the verification loss and prediction loss of the proposed method are reduced compared with the current mainstream technology, and its actual prediction performance is greatly improved, which has positive significance for the development of industrial equipment health prediction technology.