Affected by factors such as complex production operation data, high dimensions, and weak regularity, the existing ultra-short-term working condition prediction method struggles to guarantee the prediction accuracy and operation speed. Therefore, we propose an ultra-short-term working condition prediction method based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). Firstly, we use sliding window and normalized processing methods to carry out data processing, and use CNN to extract the characteristics of processed production operation data. Secondly, we then improve the LSTM gated structure and introduce L2 norm, learning the change law of the production operation data by means of the LSTM prediction layer, and then obtain the predicted value of the working condition. We use the Bayesian method to select the parameters of the CNN-LSTM model to improve the prediction accuracy. Finally, we apply our method to a real-world application to demonstrate that our ultra-short-term working condition prediction method achieves superior results for prediction accuracy and running speed when compared with other methods.