In order to solve the problems of blurred boundaries and insufficient accuracy in ore image segmentation caused by complex texture， irregular shape and uneven illumination， a segmentation network with Linear Deformable Convolution （LDConv） and dual-domain synergistic dynamic attention was proposed， namely LDDA-Net （Linear Deformable Dual-domain Attention Network）. LDDA-Net adopted an encoder-decoder architecture. In the serial dual-feature encoder， an adaptive sampling point distribution was constructed through LDConv to flexibly fit the irregular shapes of the ore， and effectively control the computational overhead with its linear characteristics. Secondly， a Dynamic Attention Modulation （DAM） module was designed for spatial domain features， which realized dynamic focusing and reinforcement of the key information in the feature map and the ore edge through pooling sampling， learnable attention matrix and boundary-sensitive weight allocation mechanism. Finally， a new Dynamic Progressive Attention Guided Loss function （DPAG Loss） was proposed， which guided the model to focus on hard-to-divide areas such as fuzzy boundaries and small-sized ore particles during the training process by dynamically generating attention maps in multiple stages， and a space-loss dual-domain synergy was formed by DPAG Loss and DAM module， creating a feedback closed-loop mechanism of feature perception and learning strategies. Experimental results on the self-built open-pit ore dataset （OpenPitOre dataset） and the public ore dataset （Ore dataset） showed that LDDA-Net achieved a HD95 boundary error of only 16.84 mm， which is 11.37% lower than that of the suboptimal model VM-Unet； it attained the Dice coefficient as high as 91.54%， the mIoU and PA of 85.13% and 94.10%， respectively， significantly outperforming comparative segmentation models. LDDA-Net achieves high-precision and refined segmentation in complex scenarios， providing reliable technical support for intelligent detection and fragmentation analysis of ore in open-pit blasting.