Method of Entrepreneurship Program Education for College Students Based on Deep Learning

Abstract

With the development of social economy and technological progress, the integration of higher education and industrial demand has become an important way to enhance the entrepreneurial ability of college students. Currently, universities are manually selecting industries and entrepreneurship majors that integrate industry and education. The application of neural networks has brought new opportunities for the integration of industry and education. We propose a method of integrating industry and education based on neural networks, which optimizes the allocation of educational resources through neural networks and improves the entrepreneurial abilities of college students. By utilizing the deep learning capabilities of neural networks, the corresponding entrepreneurial needs can be intelligently matched with educational resources to achieve precise and personalized educational guidance. The superiority of the algorithm proposed in this study was verified through experiments. The classification accuracy has been improved to 95.5%, with an F1 score of 94.2%, which is 4.6% higher than traditional methods such as multi-layer perceptron. The coverage rate of the recommendation system is 87%, and the novelty index is 0.76, both of which are better than existing models. The success rate of student entrepreneurship has increased from 82.1% to 98.9%, and user satisfaction has increased to 99.1%.