Objective To establish a Bayesian classifier-based lung cancer prediction model, and to discuss its predictive efficiency. Methods Using the reaction data of previously screened 6 phage peptide clones with the sera of 90 lung cancer patients and 90 healthy controls, we established a Bayesian classifier-based lung cancer prediction model, with the data analyzed by BinReg 2.0 software. The predictive efficiencies of different models (Bayesian classifier-based prediction model，Logistic regression, principal component regression, and support vector machine) were evaluated by receiver operating characteristic (ROC) curves. Results The sensitivity and specificity of Bayesian classifier-based lung cancer prediction model were 92.00% and 96.00%, respectively. And the model satisfactorily distinguished lung cancer patients and healthy controls. Conclusion Our Bayesian classifier-based lung cancer prediction model can accurately predict the risk of lung cancer.