There are many development wells in mature oilfields with multiple series of strata in the vertical direction and complex oil–water relationships. Thus，the manual interpretation of stratigraphic correlation has heavy workload and multiple solutions. In conventional research based on big data，a sample prediction model is established at one time with multiple logging curves and layered sample labels by a selected machine learning algorithm. However，this method has low accuracy and difficult convergence. To tackle the problems，this paper proposes a small layer intelligent division method based on data-driven and cyclic sliding time window. The logging curves sensitive to geological stratification are selected as the characteristic parameters. To enrich the sample database，the paper collects sample data many times with the“window-topoint” circular sliding time window method. The optimal traimodel is obtained by optimizing the hyper-parameters of different machine learning algorithms. The model is used to predict the results of small layer partitioning. Analysis results show that when the length of the sliding time window is 20 ning and the step size is 2，the small layer intelligent division model based on the random forest method has a prediction accuracy of 88.4%，which is better than the conventional prediction method based on modeling at one time and achieves the best test effect.