In order to solve the problems of time-consuming processes and insufficient accuracy in the surface porosity recognition technology of the cladding layer, A 2BNC-Unet neural network based on the U-Net neural network is proposed. The goal is to identify pores on the cladding layer's surface using semantic segmentation in deep learning technology. By introducing the Batch Normalization layer and the Convolutional Block Attention Module (CBAM) into the neural network in a reasonable manner, the Intersection over Union (IoU) and Dice coefficient were selected as evaluation indicators for the network. The results show that, in the test set, the intersection over union and Dice coefficient of the 2BNC-Unet network are 86.96% and 86.42%, respectively, which are 7.65% and 4.73% higher than those of the U-Net neural network. Additionally, to verify the performance of the network, comparative experiments were conducted using SegNet, 2BNC-Unet, and U-Net neural networks. The results demonstrate that the segmentation effect of 2BNC-Unet is not only better than that of SegNet and U-Net networks but also capable of completely segmenting the pore details on the cladding layer's surface. In the realm of deep learning technology, the segmentation speed and accuracy of 2BNC-Unet have been significantly improved, providing assistance in the performance analysis of cladding layers through pore segmentation.