Construction and empirical analysis of project schedule control model based on machine learning and deep learning

In this study, a project schedule control model based on machine learning and deep learning techniques is constructed and its effectiveness is verified through empirical analysis. The model realizes accurate prediction of project schedule by integrating the data processing ability of machine learning and the complex relationship capturing ability of deep learning. The study adopts the RFE-SVM algorithm for feature selection, the PCA algorithm for dimensionality reduction, and combines GBDT, random forest and deep neural network to construct a composite model. The empirical results show that the model has significant advantages in improving prediction accuracy and stability and effectively reduces schedule risk in real projects. Despite the limitations in generalization ability and interpretability, the model provides intelligent and accurate decision support for project management and promotes the innovative development in the field of project management.