Prediction of ROP is of great significance for optimizing drilling technology， reducing operating costs， and realizing scientific drilling， and it is also an important part of drilling operations. The accuracy of ROP prediction with artificial intelligence technology is remarkable， but the massive data required by this technology puts forward higher requirements for the traditional drilling operations. To determine the minimum amount of data for ROP modeling based on artificial intelligence， analysis was carried out based on 21917 data samples collected from 10 wells at South China Sea. Through correlation analysis， all input parameters were divided into three categories： high， medium and low correlation. By gradually introducing parameters to establish a prediction model to compare the accuracy， it was found that when the number of parameters was sufficient， the parameters in all three categories can be used to establish a high-precision （≥85%） prediction model； however， the higher the correlation of the parameters， the less the number of the parameters required to set up a high accuracy prediction model. When the sampling interval is gradually expanded， comparison found that the accuracy of all the prediction models decreased with the sampling interval increased. The lower limit of the data sampling interval for setting up the prediction model can be obtained through finding out the downward inflection point of prediction accuracy. It is verified that the BP neural network prediction model based on any of the three correlation parameters can still obtain high prediction accuracy when both data dimension and sampling accuracy are at the lower limits.