The　sparrow　search　algorithm　is　improved　to　address　its　decrease　of　population　diversity　in　the　later　stage　and　its　easy　fall　into　the　local　optimal　solution.　The　improved　algorithm　introduces　the　oppositional　learning　strategy　based　small　hole　imaging　to　update　the　discoverer’s　position,　enhancing　the　diversity　of　the　optimal　position.　Then,　inspired　by　the　Logistic　model,　a　new　adaptive　factor　is　proposed　to　dynamically　control　the　safety　threshold,　thus　balancing　the　global　search　and　local　development　capabilities　of　the　algorithm.　Simulations　of　comparison　with　other　algorithms　in　six　benchmark　functions　are　conducted,　and　experimental　results　show　higher　convergence　accuracy　and　speed　of　the　improved　algorithm　than　those　of　the　other　algorithms.　In　engineering　applications,　the　proposed　algorithm　optimizes　the　K-means　clustering　algorithm　for　image　segmentation　with　satisfactory　segmentation　performance　in　terms　of　peak　signal　to　noise　ratio　(PSNR),　structural　similarity　(SSIM)　and　feature　similarity　(FSIM).　©　2023　Beijing　University　of　Aeronautics　and　Astronautics　(BUAA).　All　rights　reserved.