Prenatal physical examination of pregnant women is a import part of perinatal medicine. Prenatal prediction of fetal weight can provide an accurate reference for judging the healthy development of the fetus. The multiple physical examination records have the characteristics of variable time interval distribution during gestational period. This study proposes a variant of LSTM model, Variable Time Interval LSTM (VTI-LSTM), to solve the variable time intervals problem. The data of this study were from 122 462 medical records of 10 473 pregnant women from several women's hospitals during 2015 to 2018. The experiments of fetal weight prediction compare the traditional formula estimation methods with the machine learning methods such as GBDT, MLP, SVR, RNN, LSTM, and VTI-LSTM. The results show that Variable Time Interval LSTM has a good prediction result in the prediction of low birth-weight fetal and macrosomia.