Postpartum hemorrhage in pregnant women is one of the most important factors of maternal death around the world, ranking first in China. However, the early diagnosis of postpartum hemorrhage has always been a medical problem. With the popularity of Electronic Health Records and the development of machine learning and deep learning technologies, new solutions have been provided for predicting postpartum hemorrhage in pregnant women. This study proposes to construct a mixed prediction model of postpartum hemorrhage based on LSTM and XGBoost by using the Electronic Health Records of pregnant women. The experimental results show that the hybrid model based on LSTM and XGBoost is feasible to predict postpartum hemorrhage in pregnant women. It can provide a reference for doctors to judge the situation of postpartum hemorrhage and provide decision-making support for whether blood preparation would be needed during delivery. It is of positive significance to reduce the mortality rate of postpartum hemorrhage.