Intelligent Talent Cultivation Quality Evaluation in Digitalized Educational Settings

This study proposes an intelligent assessment system based on convolutional neural networks (CNN) for automated assessment of students' learning outcomes in a digital education environment. By analyzing multi-dimensional learning data (such as attendance, homework completion, and test scores), the system can effectively evaluate students' performance and provide real-time feedback to educators. Experimental results show that the CNN model outperforms traditional methods in terms of assessment accuracy, processing efficiency, and scalability. This paper further discusses the improvement of educational management efficiency by the system and its potential for application in future educational environments, and recommends the introduction of more advanced models such as long short-term memory networks (LSTM) to expand the functionality and adaptability of the system.