According to the distribution characteristics of remaining oil in high water cut oilfields，the pre-making method of fitting samples of oil saturation isolines and the remaining oil prediction method based on the artificial neural network are proposed. This paper applies the numerical simulation method to generate the remaining oil distribution fields between injection and production well groups under different well spacings，physical properties，working systems，and other conditions in batches. It programs a module to automatically extract a small amount of oil saturation isolines and constructs fitting parameter sample data set by polynomial functions to fit the oil saturation isolines at different times and horizon. This method can reduce the sample parameters of machine learning. Tensorflow is adopted to construct the neural network model. After the learning and training process，the oil saturation isoline prediction model between injection and production well groups is formed. The oil saturation field is reconstructed according to the superposition results of isoline maps between multiple well groups. Comparison between the actual data of high water cut oilfield and numerical simulation shows that the new method has prediction ability for fault boundary，interlayer residual oil enrichment area，and local scattered remaining oil between wells. This method can quickly convert dynamic data of oil and water wells into saturation field data. Compared with the traditional method，the proposed method significantly improves the calculation speed and quantification.