Objective We aimed to build a multi-parameter genes diagnostic model of hepatocellular carcinoma based on peripheral blood mRNA. is pressing. Methods GeXP method was used to detect the expression of 9 genes (GPC3, HGF, ANXA1, FOS, SPAG9, HSPA1B, CXCR4, PFN1 and CALR). 103 early stage of HCC patients and 54 age matched health normal control were used to build the diagnosis model. 52 early stage of HCC patients and 34 healthy people were used for validation. Logistic regression analysis, discriminant analysis, classification tree analysis and artificial neural network were used for the multi-parameter genes expression analysis method. The ROC curve, area under the curve, sensitivity and specificity were used as the diagnosis indicators. Results: Artificial neural network of the total 9 genes had the best diagnosis value, the AUC, sensitivity and specificity were separately 0.943, 98% and 85%. At last, 52 HCC patients and 34 Healthy normal controls were used for validation. The sensitivity and specificity were separately 96% and 86%. Conclusions: Multi-parameter analysis methods may increase the diagnosis value compared to the single factor, and it may be potential assistant diagnostic method.