The traditional prediction models for the corrosion rates of industrial pipelines often have the problems of dependence of feature extraction on artificial experience and insufficient generalization ability. To address this issue, this study combines the convolutional neural network (CNN) with the long short-term memory (LSTM) network and proposes a network model based on the cuckoo search (CS) optimization algorithm, namely, the CNN-LSTM-CS model, to predict the corrosion rates of industrial pipelines. Specifically, the collected pipeline corrosion dataset is pre-processed by normalization. Then, the CNN is used to extract information on the deep features of factors affecting the corrosion rates of the pipelines, and a CNN-LSTM prediction model is constructed by training the LSTM network. Finally, the CS algorithm is used to optimize the parameters of the prediction model, thereby reducing the prediction error and predicting the corrosion rate accurately. The experimental results show that compared with several typical prediction methods for the corrosion rate, the method proposed has higher prediction accuracy and provides a new approach for predicting the corrosion rates of industrial pipelines.