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The rise of the digital era presents mounting difficulties for ideological and political instruction, as the swift dissemination of internet-based public sentiment and the expanding reach of social media frequently outpace conventional pedagogical approaches. Present methodologies suffer from poor precision in detecting risks and lack explicit metrics for assessing the fundamental competencies of instructors. To surmount these challenges, this paper presents a dual-core system framework. Chiefly, the Multi-modal Decision Support System (MADF-SE) merges classroom behavioral identification, semantic evaluation, and blockchain-enabled traceability to enhance both the precision and punctuality of hazard detection. Secondary to this, the Dynamic Capability Evolution Model (DCEM) quantitatively monitors and upgrades the cognitive, practical, and inventive skills of teachers over time. Furthermore, a hierarchical federated learning architecture is deployed to strike a balance between individual privacy safeguarding and inter-collegiate cooperation. Empirical testing and case analyses across more than 30 higher education institutions indicate that the introduced framework cuts the public opinion management timeframe by 50%, elevates instructor engagement in strategic choices from 20% to 60%, and boosts their collective core competencies by greater than 25% on an annual basis. These outcomes highlight a viable and scalable methodology for driving the digital evolution of ideological and political schooling.