Addressing the challenges of ore recognition in the mining industry and the high cost of recognition equipment, we propose an improved Unet ore image segmentation algorithm. Firstly, we modify EfficientNetV2 as the backbone network of the model to extract ore features. Secondly, we introduce the MBconv module as the decoder, enhancing the feature extraction capability. We then replace the SE attention module with the CA attention module to retain more spatial position information. Finally, we substitute the commonly used Batch Normalization (BN) layer with the Filter Response Normalization (FRN) layer to prevent model performance from being affected by batch size. Experimental results based on FeM and Cu datasets demonstrate that our proposed model achieves a PA of 96.58% and 95.39%, an MIoU of 92.8% and 90.43%, and an F1 score of 95.15% and 93.47%. Compared to Unet, the Efficient_Unet model parameters are reduced by 60.4%, and the inference speed is improved by 19.23%, reaching 21.7 frames per second. Our proposed model outperforms existing classical segmentation models in terms of accuracy and speed, exhibiting strong generalization performance.