Application Research on Quality Evaluation of College Graduation Thesis Based on Artificial Intelligence

This article focuses on the optimization of the quality evaluation system for college graduation theses, and proposes a solution based on artificial intelligence technology to address the limitations of traditional manual evaluation methods, such as subjectivity, low efficiency, and inconsistent evaluation standards. A quality evaluation model for graduation thesis was developed, which integrates features of layout, writing style, and text semantics. This model utilizes natural language processing and machine learning techniques to automate the processing and analysis of graduation thesis data, aiming to improve the objectivity, accuracy, and efficiency of evaluation. Specifically, the layout features focus on the visual presentation of the paper, while the writing style features emphasize language expression and argumentation, and the deep semantic features delve into the academic value and innovative points of the paper. By integrating these three characteristics, the model can comprehensively and deeply reflect the quality level of the graduation thesis. To verify the effectiveness and reliability of the model, this study borrowed Tencent's CoQAN model idea for experimental verification. The experimental results show that the model has significant accuracy and stability in the quality evaluation of graduation theses, providing strong technical support for the optimization of the quality evaluation system of university graduation theses. In addition, the application prospects and challenges of artificial intelligence technology in the quality evaluation of graduation theses were discussed, and further research directions and suggestions were proposed.