Transformation Path of Artificial Intelligence Algorithms and Intelligent Engineering Management

In response to the low accuracy problem faced by the current engineering management field in project schedule management and other aspects, this article introduces the research on artificial intelligence algorithms and intelligent transformation paths, aiming to optimize project management processes and improve efficiency. Firstly, the core issues of project progress, resource allocation, and risk control in engineering management can be systematically identified. Then, this article designs an intelligent decision support system based on machine learning and deep learning, constructs a multidimensional prediction model, and achieves project key milestone prediction, resource scheduling optimization, and potential risk analysis. Finally, an intelligent management framework that adapts to projects of different scales is introduced. Through data collection, real-time analysis, and reinforcement learning dynamic decision support mechanisms, iterative optimization of resource allocation and risk control is achieved. At time points 3, 4, 6, and 9, the probability of actual risk events occurring increases, and the prediction accuracy of the Bayesian network model significantly improves. Its predictions at these time points almost completely match the actual situation (the predicted values are the same as the actual values). The combination of artificial intelligence algorithms and intelligent transformation paths provides new development opportunities for the field of engineering management. By gradually building an intelligent management system, not only can the management level of engineering projects be effectively improved, but also the optimal allocation of resources, precise control of risks, and efficient achievement of project goals can be achieved.