Research on investment project risk prediction and management based on machine learning

In the era of digital economy, digital transformation is an inevitable choice in line with current economic development and national policy trends. Enterprises use new generation digital technologies such as big data, blockchain, cloud computing, artificial intelligence, and financial technology to apply these technologies to various production and business activities. The research on project risk management has gradually introduced the method of intelligent decision-making, using technologies such as big data and artificial intelligence to identify and analyze risks. We use learning models and ensemble learning techniques to predict and manage the risks of investment projects. Through long short-term memory networks (LSTMs), we are able to effectively extract spatiotemporal features from historical investment data to predict future risk dynamics. In order to further improve the accuracy of prediction and the robustness of the model, we introduced the Gradient Booster (GBM) ensemble learning method. These integrated technologies not only optimize the overall performance of the model, but also enhance the adaptability and forecast accuracy of various market changes. In the experimental analysis, we compare the performance of multiple models on real-world investment datasets, and the results show that the ensemble learning method has significant advantages in risk prediction accuracy.