Objective Chronic rhinosinusitis with nasal polyps (CRSwNP) is a global health problem, and the existing diagnostic techniques have some limitations. Therefore, it is necessary to develop new diagnostic models to supplement the existing diagnostic methods.Methods The public gene expression data of CRSwNP patients (GSE23552, GSE36830) were used to identify potential differential genes. The random forest algorithm and artificial neural network were used to screen specific genes and establish the early diagnosis model of CRSwNP.Results A total of 78 up-regulated genes and 25 down-regulated genes were identified. Four specific genes (HPGDS, IL1RL1, FMO3 and DOK3) were screened by random forest algorithm. The prediction model based on the above genes was constructed by artificial neural network, which had good prediction effect (area under the curve=0.986). Independent dataset GSE194282 further verified the accuracy (area under the curve=0.888).Conclusions A predictive model based on gene expression level is established by machine learning method. This model can predict early CRSwNP, which is helpful for early diagnosis and clinical decision.