Surface-enhanced Raman spectroscopy (SERS) is a surface-sensitive technique that enhances Raman scattering by molecules adsorbed on nanostructures. The advantages of using SERS include high detection sensibility and fast analysis, thus it is a potentially promising tool for sensing metabolic cancer molecules in trace amounts. To explore this new method of lung cancer detection, we analyzed saliva samples from 61 lung cancer patients and 66 healthy controls. An SERS system and a nano-modified chip were used in this study. Statistics were analyzed using support vector machine (SVM) and random forest algorithms. The leave-one-out algorithm was used based on SVM results to analyze differences in saliva between lung cancer patients and controls. There was a significant difference between the saliva of patients with lung cancer and healthy controls using the Raman spectrum; the intensity of the spectral line in lung cancer patients was weaker than in controls and 12 characteristic peaks were detected. Saliva SERS peaks have been characterized to refer to tissues, body fluids, and biological standard Raman peaks, but it is difficult to identify molecules with current information. The sensitivity and specificity of Raman spectroscopy data and SVM classification results of lung cancer patients and normal saliva samples were both 100%. Using the leave-one-out algorithm, the sensitivity was 95.08% and the specificity was 100%. The sensitivity of the random forest method was 96.72% and specificity was 100%. Our results show that SERS has the potential to detect lung cancer.