Most attention mechanisms, while enhancing image features, do not consider the impact of local feature interaction on overall feature representation. To address this issue, this paper proposes a global pooling residual classification network guided by local attention (MSLENet). The baseline network for MSLENet was ResNet34. First, the initial layer structure was modified to retain important image information. Second, a multiple segmentation local enhancement attention mechanism (MSLE) module was introduced. The MSLE module first segmented the image into multiple small images, then enhanced the local features of each small image, and finally integrated these important local features into the global features through feature group interaction. Lastly, a pooling residual (PR) module was proposed to address the information loss problem in the ResNet residual structure and improve the information utilization between layers. The experimental results show that by enhancing the interaction of local features, MSLENet achieves good performance on multiple datasets and effectively improves the expressive ability of the network.