Abstract: This article explores the application of artificial intelligence in risk management of power grid projects, and proposes an artificial intelligence risk warning method based on data comparison analysis to address the limitations of traditional risk list method and Delphi method. By collecting, organizing, and comparing multi-source data, a dynamic risk assessment model is constructed to achieve automatic identification, evaluation, and early warning of risks. Case analysis shows that this method can provide early warning of risks, improve warning accuracy, provide support for project management decisions, and ensure the smooth implementation of power grid projects.