Thymic epithelial tumors (TETs), rare yet clinically significant malignancies, face diagnostic challenges due to their occult presentation and lack of noninvasive risk-stratification tools, leading to systemic overtreatment and poor prognoses for high-risk subtypes. To address this unmet need, we developed a Fe3O4@Fe metal-organic framework heterojunction-enhanced laser desorption ionization mass spectrometry (LDI MS) platform for the efficient analysis of serum metabolic fingerprints (SMFs). Engineered through gradient pyrolysis, this nanomaterial synergizes ultraviolet absorption and photothermal conversion from its two constituent components with enhanced charge separation, achieving 1,000-fold improvement in sensitivity and thus enabling direct SMF acquisition from 1 μL of serum. Coupled with machine learning, the platform demonstrates robust diagnostic performance, yielding area under the curve (AUC) of 0.960 for distinguishing TETs from benign control and AUC of 0.856 for hierarchical risk stratification, outperforming clinical workflows. Beyond advancing material design for LDI MS, this work establishes a clinically translatable framework for rapid, large-scale screening, addressing critical gaps in TET management through metabolic-driven stratification.