Abstract: Deepwater drilling has difficulties such as high investment and high risks. Real-time intelligent rig activities classification of deep-water drilling is the basis and premise of improving drilling efficiency and reducing complex accidents. In traditional deep-water drilling field operation, the rig activities are mainly classified by physical models and empirical models based on programming, which is difficult to ensure the timeliness and accuracy. Therefore, the method of machine learning is innovatively introduced into the whole process of deep-water drilling rig activities classification in this paper. Considering the long time series characteristics of comprehensive mud-logging data, a real-time intelligent machine learning model for deep-water drilling rig activities classification was established based on the Long Short-Term Memory (LSTM) network. After the preprocessing of 29,856,140 lines of deep-water comprehensive mud-logging data, there were eight comprehensive mud-logging parameters selected as input features, including Depth of drill bit in real-time (DBTM), Measured depth of hole (DMEA), Hook Height (HKH), Weight on Bit (WOB), Weight on Hook (WOH), TORQUE, Rate per Minute (RPM) and Standpipe Pressure (SPP). Then, a LSTM network model with 20 hidden layers ×70 nodes was established. It has realized the real-time intelligent classification of 12 common deep-water drilling rig activities, including rotary drilling, slide drilling, stand connection, static, circulate, wash down, ream, wash up, backream, trip out, trip in, and other. The accuracy of machine learning model in the testing dataset is as high as 94.09%, which meets the requirements of deep-water field operations. The rig activities were intelligently classified in real-time, which fully verifies the feasibility and timeliness of the LSTM network for the real-time intelligent classification of rig activities. Furthermore, the real-time intelligent method provides the machine learning models basis for the drilling efficiency analysis and complex accidents warning, which will expand the application range of machine learning in the petroleum engineering.