Acute liver injury is a common hepatic condition that, without timely diagnosis and treatment, can easily progress to life-threatening liver failure. Identifying key biomolecular changes in the pathogenesis of liver injury is critical for the precise diagnosis of hepatic injury. In this study, we identified novel molecular fingerprints associated with hepatic injury using artificial intelligence-assisted Surface-enhanced Raman spectroscopy (SERS) technology. By intravenous injection of PEG-modified Au nanoparticles, which efficiently accumulate in the liver, these nanoparticles act as in vivo computed tomography contrast agents, specifically delineating the three-dimensional structure of normal and injured liver tissues. Additionally, as a Raman-enhanced substrate, gold nanoparticles allow for the analysis of SERS signals in liver tissues enriched with these nanoparticles. Leveraging artificial intelligence technology, we achieved a classification accuracy of 96.45% for different degrees of liver injury. Molecular spectral analysis revealed that the ratio of the 1437-1000 cm