Imparting traditional wisdom and political knowledge through deep tracking and knowledge graph model

In the context of the intelligent transformation of ideological and political education, how to achieve objective prediction of students' knowledge mastery has become a research difficulty. In response to the problem of insufficient model generalisation caused by traditional methods relying on non-public data, this study proposes a prediction model for ideological and political knowledge mastery based on deep tracing and knowledge graph (DKP model). Firstly, extract the temporal characteristics of learning behaviour and the logical correlation between knowledge points. Secondly, design a dynamic knowledge tracking framework and introduce the knowledge graph attention network (KGAT) to model the dialectical relationships and cognitive transfer paths between ideological and political knowledge points. Further, construct an interpretable mastery prediction index system. The cross-domain data fusion paradigm and open-source knowledge graph construction method proposed in the study provide a reproducible technical framework and infrastructure support for educational equity research.