This work presents the Forensic Oil Master—an open-source, forensic-grade multi-modal antique authentication system designed to eliminate counterfeiting in the antique market and replace subjective expert judgment with data-driven objectivity. Integrating four core scientific detection modalities (AFM atomic force microscopy, GC-MS gas chromatography-mass spectrometry, infrared/Raman spectroscopy, microscopic image analysis) and AI fusion decision-making, the system achieves quantitative authentication with 94.3% accuracy, <2% false positive rate, and <3% false negative rate, fundamentally solving the flaws of traditional subjective appraisal. The technical framework operates with only 0.2mg of micro-samples: 1) AFM captures nanoscale mechanical "fingerprints" (adhesion, hysteresis, slope) of natural patina, distinguishing genuine (-0.15~-0.21nN adhesion) from artificial aging (-0.05~-0.10nN); 2) GC-MS identifies chemical "ID cards" by detecting degradation products and modern pollutants (e.g., plasticizer m/z=149); 3) Spectroscopy analyzes chemical bond changes (e.g., 1650cm⁻¹ conjugated double bonds unique to genuine antiques); 4) Microscopic imaging extracts texture entropy and crack distribution to expose artificial brushing. Data is fused via weighted voting (AFM 35%, GC-MS 30%, spectroscopy 20%, image 15%) with ternary decision output (Authentic/Fake/Uncertain), and a blockchain audit ledger ensures tamper-proof reports. Fully open-source under MIT License, the system supports edge deployment, batch detection, and custom model training. Validated through real cases (e.g., identifying a "Qing Qianlong Huanghua Pear Cabinet" as fake with 0.97 confidence), it raises counterfeiting costs to unprofitable levels, empowering collectors, auction houses, and regulators with accessible scientific authentication—marking the end of the "expert-dominated" era and the dawn of data-driven antique appraisal.