Kidney　stones　are　a　common　urological　disease　with　an　increasing　incidence　worldwide.　Traditional　diagnostic　methods　for　kidney　stones　are　relatively　complex　and　time-consuming,　thus　necessitating　the　development　of　a　quicker　and　simpler　diagnostic　approach.　This　study　investigates　the　clinical　screening　of　kidney　stones　using　Surface-Enhanced　Raman　Scattering　(SERS)　technology　combined　with　multivariate　statistical　algorithms,　comparing　the　classification　performance　of　three　algorithms　(PCA-LDA,　PCA-LR,　PCA-SVM).　Urine　samples　from　32　kidney　stone　patients,　30　patients　with　other　urinary　stones,　and　36　healthy　individuals　were　analyzed.　SERS　spectra　data　were　collected　in　the　range　of　450-1800　cm(-1)　and　analyzed.　The　results　showed　that　the　PCA-SVM　algorithm　had　the　highest　classification　accuracy,　with　92.9　%　for　distinguishing　kidney　stone　patients　from　healthy　individuals　and　92　%　for　distinguishing　kidney　stone　patients　from　those　with　other　urinary　stones.　In　comparison,　the　classification　accuracy　of　PCA-LR　and　PCA-LDA　was　slightly　lower.　The　findings　indicate　that　SERS　combined　with　PCA-SVM　demonstrates　excellent　performance　in　the　clinical　screening　of　kidney　stones　and　has　potential　for　practical　clinical　application.　Future　research　can　further　optimize　SERS　technology　and　algorithms　to　enhance　their　stability　and　accuracy,　and　expand　the　sample　size　to　verify　their　applicability　across　different　populations.　Overall,　this　study　provides　a　new　method　for　the　rapid　diagnosis　of　kidney　stones,　which　is　expected　to　play　an　important　role　in　clinical　diagnostics.