Since　the　launch　of　Chinese　Human　Proteome　Project　(CNHPP)　and　Clinical　Proteomic　Tumor　Analysis　Consortium　(CPTAC),　large-scale　mass　spectrometry　(MS)　based　proteomic　profiling　of　different　kinds　of　human　tumor　samples　have　provided　huge　amount　of　valuable　data　for　both　basic　and　clinical　researchers.　Accurate　prediction　for　tumor　and　non-tumor　samples,　as　well　as　the　tumor　types　has　become　a　key　step　for　biological　and　medical　research,　such　as　biomarker　discovery,　diagnosis,　and　monitoring　of　diseases.　The　traditional　MS-based　classification　strategy　mainly　depends　on　the　identification　and　quantification　results　of　MS　data,　which　has　some　inherent　limitations,　such　as　the　low　identification　rate　of　MS　data.　Here,　a　deep　learning-based　tumor　classifier　directly　using　MS　raw　data　is　proposed,　which　is　independent　of　the　identification　and　quantification　results　of　MS　data.　The　potential　precursors　with　intensities　and　retention　times　from　MS　data　as　input　is　first　detected　and　extracted.　Then,　a　deep　learning-based　classifier　is　trained,　which　can　accurately　distinguish　between　the　tumor　and　non-tumor　samples.　Finally,　it　is　demonstrated　the　deep　learning-based　classifier　has　a　good　performance　compared　with　other　machine　learning　methods　and　may　help　researchers　find　the　potential　biomarkers　which　are　likely　to　be　missed　by　the　traditional　strategy.