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Early diagnosis is crucial to improve outcomes for pancreatic cancer (PC) patients, however the lack of specific and validated biomarkers for the disease remains challenging. In this study, we analyzed the serum proteome of 22 PC patients, 12 pancreatitis patients (PP) and 45 healthy controls (HC), using magnetic bead-based weak cation exchange and matrix-assisted laser desorption ionization-time of flight mass spectrometry. Next, we established supervised neural network (SNN) algorithm model by ClinProTools and identified the candidate biomarker using liquid chromatography-electrospray ionization-tandem mass spectrometry. Finally, candidate biomarker was validated in tissue samples. The SNN algorithm model discriminated PC from HC with 92.97% sensitivity and 94.55% specificity. We identified 76 differentially expressed peptides, 7/76 peaks were significantly different among PC, PP and HC (P