Abstract: A new generation of intelligent technology is driving the paradigm change of organizational management, among which the tacit knowledge mining technology based on machine learning provides a breakthrough methodology for the value transformation of organizational knowledge capital. Based on the analysis of the characteristics of risk factors in knowledge management of engineering projects, through empirical research, the knowledge management risk early warning system is divided into three levels: 1) Normal Risk Level: The knowledge management operating environment is stable, posing no significant threat to business development. At this stage, risk prevention and dynamic monitoring are the main strategies; 2) Risk Warning Level: The uncertainty of knowledge management risk factors increases significantly, causing substantial losses to business operations. At this point, the early warning mechanism needs to be activated, and targeted response measures should be implemented; 3) Risk Crisis Level: Knowledge management risks have materialized, and the damage to business operations and development exceeds the preset threshold. At this stage, emergency plans must be initiated immediately, and crisis management measures should be taken to maximize the protection of corporate interests and minimize losses. The research validates the practicality and effectiveness of this early warning mechanism in risk analysis and assessment through specific engineering case studies.