The accurate extraction of core regions is of great significance for tasks such as digital core construction and intelligent reservoir evaluation. However, core images often suffer from complex backgrounds, blurred edges, and multi-scale structural distributions, posing significant challenges to automated segmentation. To address these issues, this paper proposes a core object extraction algorithm based on an improved UNeXt architecture, aiming to enhance the model′s segmentation performance for core regions. The method effectively strengthens the model′s ability to represent edge details and global contextual features by introducing CBAM or EMA modules at different network levels. Simultaneously, a multi-scale feature enhancement module is designed and incorporated into the network neck to further improve the model′s perception of multi-scale structures and complex textures. Additionally, given the current lack of publicly available datasets dedicated to core region extraction, this paper independently constructs a relevant core image dataset. Experimental results show that, compared to the original UNeXt network, the proposed algorithm achieves improvements of 1.49%, 2.06% and 0.75% in mIoU, F1-score and mPA, respectively, while the MSE decreases by 78.63%. Statistical tests confirm that these improvements are all significant. To validate the model′s generalization capability, comparative experiments were also conducted on two public medical image datasets, BRISC 2025 and EBHI-Seg. The results demonstrate that the proposed algorithm performs excellently on both the self-constructed dataset and the public datasets.