The　liver　is　one　of　the　organs　with　the　highest　incidence　rate　in　the　human　body,　and　late-stage　liver　cancer　is　basically　incurable.　Therefore,　early　diagnosis　and　lesion　location　of　liver　cancer　are　of　important　clinical　value.　This　study　proposes　an　enhanced　network　architecture　ELTS-Net　based　on　the　3D　U-Net　model,　to　address　the　limitations　of　conventional　image　segmentation　methods　and　the　underutilization　of　image　spatial　features　by　the　2D　U-Net　network　structure.　ELTS-Net　expands　upon　the　original　network　by　incorporating　dilated　convolutions　to　increase　the　receptive　field　of　the　convolutional　kernel.　Additionally,　an　attention　residual　module,　comprising　an　attention　mechanism　and　residual　connections,　replaces　the　original　convolutional　module,　serving　as　the　primary　components　of　the　encoder　and　decoder.　This　design　enables　the　network　to　capture　contextual　information　globally　in　both　channel　and　spatial　dimensions.　Furthermore,　deep　supervision　modules　are　integrated　between　different　levels　of　the　decoder　network,　providing　additional　feedback　from　deeper　intermediate　layers.　This　constrains　the　network　weights　to　the　target　regions　and　optimizing　segmentation　results.　Evaluation　on　the　LiTS2017　dataset　shows　improvements　in　evaluation　metrics　for　liver　and　tumor　segmentation　tasks　compared　to　the　baseline　3D　U-Net　model,　achieving　95.2%　liver　segmentation　accuracy　and　71.9%　tumor　segmentation　accuracy,　with　accuracy　improvements　of　0.9%　and　3.1%　respectively.　The　experimental　results　validate　the　superior　segmentation　performance　of　ELTS-Net　compared　to　other　comparison　models,　offering　valuable　guidance　for　clinical　diagnosis　and　treatment.