The southern hilly region of China boasts abundant forest resources, which are crucial for maintaining ecological stability. However, the complex vegetation structure and fragmented terrain in this area lead to intricate and disorderly forest types, resulting in semantic confusion among vegetation in remote sensing images. Consequently, accurately classifying forest types poses significant challenges. We propose a semantic segmentation model with multiple attention mechanisms using convolutional neural networks. We enhance the U-Net model’s encoder with a deeper convolutional network to expand the receptive field without significant computation increase. Furthermore, we integrate spatial attention within the U-Net’s skip connections and multiscale feature fusion. Experimentally, the multiple attention mechanism U-Net model outperforms the original, averaging 90.67% intersection over union, 94.33% pixel accuracy, and 96.00% classification accuracy for 0.5 m resolution forest type classification. These improvements are 8.00%, 4.33%, and 5.00%, respectively. The model accurately distinguishes forest types in the southern hilly region, enabling precise information-based forest supervision.