Accurately identifying the interlayer in the reservoir is crucial for the fine characterization of the reservoir and the exploration of remaining oil potential. In order to better utilize logging data and improve the efficiency and accuracy of interlayer partitioning, this paper proposes a interlayer partitioning method based on the BA-Catboost algorithm. In the study, the general methods for identifying and dividing interlayer were compared and analyzed. In response to the difficulties of low efficiency and easy errors in manual division, the technical route of BA-Catboost algorithm was optimized and constructed. By using core logging and other data to identify interlayers and classify their types, ADASYN method was used to increase the sample size of interlayers, and high correlation logging curves such as GR, SP, and AC were selected as feature parameters. Based on the BA-Catboost algorithm, a classification model was trained and established, with model training and testing accuracies of 96.7% and 98.9%, respectively. Using a classification model to identify the feature fuzzy and difficult to divide interlayers, 62 groups of muddy interlayers, 20 groups of calcareous interlayers, and 59 groups of physical interlayers were identified. On this basis, the distribution characteristics of interlayer planes were studied, and it was found that interlayer planes were more developed in the Y2 and Y3 sub layers, showing a high frequency and density of interlayer distribution in the southeast region and a low density in the central and western regions on the plane. The use of this method to divide the interlayer makes up for the lack of understanding in previous production and development processes. Subsequently, by adjusting injection and production measures, using methods such as hole filling and increasing water injection volume, the production increase effect can be achieved. The research results show that the BA-Catboost algorithm has better performance than similar algorithms. The classification model established by this method has good training and testing effects, and is used for fine recognition and automatic classification of interlayer, improving recognition accuracy and efficiency. It can effectively guide production and development work and has application value in J area of Longdong oilfield.