Algorithmic recommendation is an important form of artificial intelligence. By analyzing information such as users’ behaviors, interests, and preferences and using digital means like collaborative filtering, content recommendation, and matrix decomposition, it can achieve precise and personalized supply to meet users’ needs. It enriches and develops the practical forms of ideological and political education tools, making it possible to implement ideological and political education in a segmented and precise manner. By transcending its purely instrumental function through “ intelligent mind-reading”, algorithmic recommendation acquires significant social attributes and emerges as a pivotal force shaping the dissemination of ideological and political education discourse. Opportunities, however, are paralleled by risks. The “ information cocoon”, “algorithmic black box”, and “ technological Leviathan” associated with the algorithmic recommendation have led to severe consequences, including the polarization of group consensus, compromised online information ethics and security, and a loss of control over discourse in online ideological and political education. Consequently, it has become imperative to master this “double-edged sword”—by skillfully guiding, utilizing, and refining algorithms to innovate, upgrade, and transform every facet of traditional ideological and political education. It is important to enhance its discursive power and guidance within the algorithmic landscape. This study constructs a synergistic empowerment framework encompassing institutional, technological, and literacy dimensions, thereby proposing a practical pathway for advancing precision - oriented digital ideological and political education under new circumstances.