Objective To build a prediction model of gestational diabetes mellitus based on support vector machine. Methods A total of 115 pregnant women who had regular checkups and deliveries at Fujian Provincial Hospital South Branch from January to December 2018 were selected, among which 50 cases of pregnant women with diabetes mellitus were selected as the observation group. 65 normal pregnant women in the same period were randomly selected as the control group. The general clinical data, blood routine indexes, coagulation function indexes and biochemistry analysis indexes of early pregnancy (8-12 weeks) were recorded, and these variables were analyzed by Pearson correlation coefficient to identify the variables included in the analysis of the prediction model. Results The top five variables with the largest absolute value of Pearson correlation coefficient between the characteristic variables and GDM were triglyceride, activated partial thromboplastin time, antithrombin Ⅲ, pre pregnancy BMI and pregnancy times, and the differences between the observation group of these five variables and the normal group were statistically significant (P<0.01).These five variables were included in the prediction model analysis by SVM algorithm, and the prediction accuracy of GDM was 78.3% and the precision was 84.6%. Conclusion The use of SVM algorithm had important clinical significance for predicting GDM in early pregnancy.