Many　spectra　have　a　polynomial-like　baseline.　Iterative　polynomial　fitting　is　one　of　the　most　popular　methods　for　baseline　correction　of　these　spectra.　However,　the　baseline　estimated　by　iterative　polynomial　fitting　may　have　a　substantial　error　when　the　spectrum　contains　significantly　strong　peaks　or　have　strong　peaks　located　at　the　endpoints.　First,　iterative　polynomial　fitting　uses　temporary　baseline　estimated　from　the　current　spectrum　to　identify　peak　data　points.　If　the　current　spectrum　contains　strong　peaks,　then　the　temporary　baseline　substantially　deviates　from　the　true　baseline.　Some　good　baseline　data　points　of　the　spectrum　might　be　mistakenly　identified　as　peak　data　points　and　are　artificially　re-assigned　with　a　low　value.　Second,　if　a　strong　peak　is　located　at　the　endpoint　of　the　spectrum,　then　the　endpoint　region　of　the　estimated　baseline　might　have　a　significant　error　due　to　overfitting.　This　study　proposes　a　search　algorithm-based　baseline　correction　method　(SA)　that　aims　to　compress　sample　the　raw　spectrum　to　a　dataset　with　small　number　of　data　points　and　then　convert　the　peak　removal　process　into　solving　a　search　problem　in　artificial　intelligence　to　minimize　an　objective　function　by　deleting　peak　data　points.　First,　the　raw　spectrum　is　smoothened　out　by　the　moving　average　method　to　reduce　noise　and　then　divided　into　dozens　of　unequally　spaced　sections　on　the　basis　of　Chebyshev　nodes.　Finally,　the　minimal　points　of　each　section　are　collected　to　form　a　dataset　for　peak　removal　through　search　algorithm.　SA　selects　the　mean　absolute　error　as　the　objective　function　because　of　its　sensitivity　to　overfitting　and　rapid　calculation.　The　baseline　correction　performance　of　SA　is　compared　with　those　of　three　baseline　correction　methods,　the　Lieber　and　Mahadevan-Jansen　method,　adaptive　iteratively　reweighted　penalized　least　squares　method,　and　improved　asymmetric　least　squares　method.　Simulated　and　real　Fourier　transform　infrared　and　Raman　spectra　with　polynomial-like　baselines　are　employed　in　the　experiments.　Results　show　that　for　these　spectra　the　baseline　estimated　by　SA　has　fewer　error　than　those　by　the　three　other　methods.　©　The　Author(s)　2021.