Low-light　image　enhancement　becomes　increasingly　important　in　computer　vision　applications,　especially　in　scenarios　such　as　nighttime　surveillance,　autonomous　driving,　and　medical　imaging.　Contextual　semantic　information　is　crucial　for　optimizing　feature　representation　and　improving　perceptual　quality,　as　it　helps　the　model　understand　the　relationships　between　objects　in　the　image　and　their　spatial　distribution,　thereby　enabling　more　effective　detail　recovery　and　enhancement　of　image　quality.　Although　the　Semantic-Guided　Zero-shot　(SGZ)　model　utilizes　semantic　information,　it　still　faces　the　issue　of　insufficient　integration　of　contextual　semantic　information,　which　leads　to　an　incomplete　understanding　of　object　relationships　and　negatively　impacts　detail　recovery　and　image　quality.　To　address　this　issue,　we　propose　a　method　that　integrates　contextual　mechanisms　into　unsupervised　semantic　segmentation　to　improve　low-light　image　enhancement　(SGZSA).　This　novel　approach　embeds　self-attention　into　the　feature　extraction　process　of　the　SGZ　model’s　Unsupervised　Semantic　Segmentation　(USS),　enhancing　its　capability　to　capture　contextual　relationships.　Our　experiments　on　benchmark　datasets　demonstrate　that　SGZSA　surpasses　existing　methods　in　both　qualitative　and　quantitative　metrics,　achieving　notable　improvements　in　brightness　and　contrast　while　retaining　fine　details.　These　results　underscore　the　effectiveness　of　self-attention　mechanisms　in　low-light　image　enhancement　and　pave　the　way　for　future　advancements　in　the　field.　©　2025　IEEE.