Method for constructing student portraits based on a hybrid neural network model
The aim of this study is to utilize a fused neural network model to predict students'overall quality and construct student profiles based on six dimensions of data.By extensively collecting personal data,including economic status,academic per-formance,lifestyle habits,mental health,extracurricular activities,and leadership skills,we conducted rigorous data preprocessing.We built a multidimensional data mining model,integrating support vector regression,gated recurrent units,and multilayer percep-trons to accurately predict students'overall quality.The research results present a comprehensive student profile,emphasizing the importance of data-driven decision-making in education and providing a scientific basis for personalized education programs.The successful validation of the fused neural network underscores its effectiveness and contributes to a deeper understanding for educa-tional decision-makers.
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