A Research on the Academic System in Universities Based on the One-Hot Encoding PAC Fuzzy Comprehensive Evaluation Algorithm

The influence of contemporary learning exhibits complex and multifaceted characteristics. This study, building upon a detailed explanation of the model, constructs a comprehensive set of evaluation indicators. By selecting survey questionnaire data from the 15th Electrical Engineering Cup on “Evaluation of the Impact of Artificial Intelligence on College Students’ Learning” as a sample, this research proposes specific steps for a study on the impact of artificial intelligence on college students’ learning based on the fuzzy comprehensive evaluation method. Initially, the one-hot encoding method ‘get dummies’ in pandas is used to process non-continuous numerical features, thereby fuzzifying the data. Subsequently, Principal Component Analysis (PCA) is employed to centralize the data, reducing its dimensionality by calculating covariance, eigenvalues, and eigenvectors. The primary components are then selected, and data is projected onto these components to obtain a dimensionally reduced dataset. The main method used in this article is PCA algorithm implementation through SVD decomposition of the covariance matrix for the purpose of data classification. The research results reveal that the fuzzy comprehensive evaluation model not only integrates subjective and objective factors but also allows for qualitative adjustments and batch processing of data, making the evaluation results more comprehensive and accurate. This effectively aids in a deeper understanding of the impact of artificial intelligence on college students’ learning and provides robust support for related decision-making outcomes.