Blood　analysis　is　crucial　for　early　cancer　screening　and　improving　patient　survival　rates.　However,　developing　an　effective　strategy　for　early　cancer　detection　using　high-throughput　blood　analysis　is　still　challenging.　Herein,　a　novel　automatic　super-hydrophobic　platform　is　developed　together　with　a　deep　learning　(DL)-based　label-free　serum　and　surface-enhanced　Raman　scattering　(SERS),　along　with　an　automatic　high-throughput　Raman　spectrometer　to　build　an　effective　point-of-care　diagnosis　system.　A　total　of　695　high-quality　serum　SERS　spectra　are　obtained　from　203　healthy　volunteers,　77　leukemia　M5,　94　hepatitis　B　virus,　and　321　breast　cancer　patients.　Serum　SERS　signals　from　the　normal　(n　=　183)　and　patient　(n　=　443)　groups　are　used　to　assess　the　DL　model,　which　classify　them　with　a　maximum　accuracy　of　100%.　Furthermore,　when　SERS　is　combined　with　DL,　it　exhibits　excellent　diagnostic　accuracy　(98.6%)　for　the　external　held-out　test　set,　indicating　that　this　method　can　be　used　to　develop　a　high　throughput,　rapid,　and　label-free　tool　for　screening　diseases.