Currently, when dealing with the classification of acute lymphoblastic leukemia (ALL), there are problems of messy background information and nuanced differences. Since it is still difficult to select key features and reduce background noise in blood sample images, it is difficult for traditional methods to capture important and subtle features, and effectively classify and identify various blood cell types, which affects the accuracy and reliability of the results. This study proposes a classification model based on ResNeXt50, which uses image enhancement to reduce background noise. The model enhances the perception of various scales and context information by improving the hole pyramid feature extraction method. By adding an improved SA attention mechanism, the model can better focus on and learn information that has a greater impact on the outcome. The model is tested on the Blood Cells Cancer public data set of Tehran (Taleqani) Hospital in Iran, and the accuracy and precision rates reach 98.39% and 98.33%, respectively. The results show that the model not only has certain clinical significance and practical value but also provides a new idea for the auxiliary diagnosis of ALL.