The distribution of remaining oil forms is of great significance for the deep development of oil fields. This study proposes a form classification method of remaining oil based on deep learning to address the problems of scarce remaining oil data and the limited ability of traditional morphological parameter classification. In the data preprocessing stage, the method uses the multi-class data generation characteristics of the generative adversarial network (ACGAN) to enhance the data of the remaining oil image. It employs the VGG19 model as the backbone network to extract deep features that cannot be described by traditional morphological parameters and introduces the SENet attention mechanism to improve the model’s feature expression ability, making the final classification results more accurate. To verify the effectiveness, the proposed method is compared with traditional classification methods based on morphological parameters and other deep learning models, and it is evaluated through subjective visual and objective indicators. The results showed that the proposed method provides a more accurate classification.