Oxytocin is the first choice for labor induction, induced abortion, and prenatal fetal monitoring. Improper dose control of oxytocin during labor can increase the risk of adverse pregnancy outcomes. However, clinical oxytocin infusion mainly depends on the manual adjustment of medical staff, leading to subjective judgment errors in doses and high human cost. In addition, the existing oxytocin injection system lacks effective intelligent control means. Therefore, this study proposes to design an intelligent program for oxytocin dose control. It can extract the features of uterine contraction signals of a fetal heart monitor, and combined with fetal heart rate, electronic medical records, nursing records and other data, a prediction model of oxytocin doses was designed based on BOA-LightGBM. The experimental results show that LightGBM optimized by Bayesian is feasible to control oxytocin doses in real time compared with the traditional model. Therefore, this study can provide decision support for obstetric medical staff to adjust oxytocin doses during labor. It plays a positive role in reducing labor costs and enabling accurate drug delivery.