Abstract: In terms of petroleum and gas resources, South China Sea is the important energy replacement area in China. However, most of the reservoirs are buried deep, and the strong plasticity of the formation under high confining pressure and the complex geological environment seriously affect the drilling efficiency. It is also very difficult to accurately predict the ROP. Hence, a set of intelligent ROP prediction model was established for the extremely thick mudstone formation with unique viscoelastic and strong plastic characteristics in South China Sea. The model took the actual data of 10 wells in an area of South China Sea as a sample. Firstly, the sample was preprocessed, and had the influencing factors excluded through outlier screening, noise reduction and standardization. Secondly, factor analysis was conducted for the five measured formation characteristics (including seismic velocity, pore pressure, fracture pressure, overbudrden pressure and formation lithology), obtaining the relationship between the five formation characteristics under three common factors. Then, based on the K-Means++ algorithm, it was concluded with the silhouette coefficient as the index that the formation clustering in this area was mainly divided into two types, the formation mainly composed of mudstone and silty mudstone, and the formation mainly composed of siltstone, fine sandstone and medium sandstone. On this basis, the KNN classification model was trained by introducing five formation characteristics to achieve the accurate prediction of formation types. Finally, Random Forest was used for different formation types to establish the ROP prediction models accordingly. Besides, Bayesian optimization algorithm was used to optimize the hyperparameters at the establishment of models, and in this way the most suitable combination of hyperparameters was found. The testing results indicate that the proposed ROP prediction model based on formation classification prediction had a R² of 0.991, E_RMS of 0.018, and E_MA of 0.011 in the data environment of the testing set. Compared with other conventional machine learning algorithms, it had higher prediction accuracy in this area. Generally, this study could provide a reference for identifying the influence of formation potential classification on the prediction accuracy of ROP.