Prenatal and postpartum emotional changes in pregnant women in early pregnancy are of great significance to the physical and mental health of mothers and infants. To identify factors related to this, we conducted this study to identify feature proteins that cause maternal depression. Boruta algorithm (BA), recursive partition algorithm (RPA), regularised random forest (RRF) algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and genetic algorithm (GA) were used to select features. Extreme gradient boosting (XGBoost), back propagation neural network (BPNN), support vector machine (SVM), random forest (RF), and logistic regression (LR) were selected to construct the predictive models. All models showed a good performance in predicting, with the mean AUC (the area under the receiver operating curve) exceeding 80%. Features will provide clues to prevent depression in pregnant women and improve the physical and mental health of mothers and babies.