Application of Embedded Neural Network in University Innovation and Entrepreneurship Education Evaluation

With the rapid development of artificial intelligence technology, especially deep learning technology, unprecedented opportunities for change have been brought to the field of educational evaluation. Embedded neural networks, as an important branch of deep learning, have demonstrated outstanding performance in many fields due to their powerful data processing capabilities, efficient pattern recognition techniques, and accurate predictive analysis capabilities. Applying it to the evaluation of innovation and entrepreneurship education in universities can not only achieve deep mining and analysis of a large amount of complex educational data, but also improve the accuracy and timeliness of evaluation. It can also provide educational managers with more scientific and comprehensive decision-making basis, helping to optimize the allocation of educational resources and formulate personalized teaching plans. By automating the processing flow, embedded neural networks can significantly improve the efficiency of evaluation work, shorten the evaluation cycle, and enable education managers to more timely grasp educational dynamics and adjust teaching strategies. By combining individual student learning data, embedded neural networks can achieve personalized evaluation of students’ innovation and entrepreneurship abilities, tailoring growth paths and development recommendations for each student.