Integrating Artificial Intelligence Technology and Humanities in Electronic Literary Studies

This investigation provides an innovative artificial intelligence (AI) concept for studying modern French electronic literary poetic works, which is consistent with the global Sustainability Management Mission and uses multidisciplinary methodologies that combine AI with conservation. It goes outside traditional relative judgments by employing characteristic extraction and machine learning technologies, specifically SVM, to create an approach for quantitatively categorizing poetic works based on aesthetically and subject-related characteristics. This investigation focuses on the system's potential to increase cross-cultural recognition and enhance poetic training, demonstrating AI's skills in electronic literary research and cross-cultural exchange. When contrasted to traditional ways, the AI technique's effectiveness suggests possibilities for maximizing resources and minimizing the environmental impact of education. Additional research will strengthen the concept's technology aspects, ensuring academic effectiveness, equal treatment, and customization. The technique's accuracy and generalizability will increase in a broader spectrum of literary styles and methods. In addition, projects will focus on equally applying AI platforms to ensure access to excellent education. This investigation shows how AI could boost poetry training and assist ecological goals. It sets the path for considerable breakthroughs in digital literary poetic and instructional creativity by addressing the identified shortcomings and adopting the technique in educational settings.