Feature recognition of student using heuristic political effective evaluation reinforcement learning algorithm

The accomplishments of the students are reflected by ideological and political emotions within the school. A major factor is the risk factors in the lack of team cohesion in the ideological and political schools, the expansion of the environmental network, and the promotion of concurrent quality assurance. In this paper, the Heuristic Political Effective Evaluation Reinforcement Learning Algorithm (HPEERLA) has been proposed to enhance group coordination, environmental network extension, and double process improvement promotion in ideological and political education. Systematic optimization analysis is integrated with HPEERLA to increase university productivity, description of practice, evaluation and academic reports in ideological and political education. The newly proposed model combines learning, association, identity, self-adaptation, and data processing, thus addressing their respective limitations. The simulation analysis is performed based on sensitivity, performance, and efficiency proves the reliability of the proposed framework. The experimental analysis of HPEERLA for Students participation in the democratic ratio is 85.36%, Students' Social Work Activities ratio as 82.74%, Students Contribution to Media in the political ratio is 88.25%, Political Engagement ratio is 89.45%, Students Efficiency in Political Evaluation ratio is 94.25%.