Quantitative Analysis of the Impact of Wireless Internet Technology on College Students’ Innovation and Entrepreneurship under the Background of “Internet Plus”

Under the background of “Internet plus,” the opportunities and challenges that college students face in the process of innovation and entrepreneurship coexist. College students should make full use of the powerful function of the Internet to excavate the huge business opportunities hidden under the background of “Internet plus.” In the context of “Internet plus” of mass entrepreneurship and innovation, the quantitative analysis method is studied in the context of wireless network technology on college students’ innovation and entrepreneurship. This paper proposes a combined weight model and an evaluation model based on genetic fuzzy optimization neural network. This research initially establishes an evaluation index system (EIS) by analyzing the influence factors of wireless network technology on college students’ innovation and entrepreneurship. In addition, EIS is also analyzed by combining the objective weight of each index obtained by the entropy with the subjective weight of each index obtained by the analytic hierarchy process to construct a combined weight model. A genetic algorithm is used to optimize fuzzy optimization neural networks and establish an evaluation index system of wireless network technology based on genetic fuzzy optimization neural network. To minimize the output error, the function of output error is used as the fitness evaluation function to output the score after several iterations. The experimental results show that the evaluation model can determine the importance of the influencing factors of wireless network technology on college students’ innovation and entrepreneurship. It is further evident from the experiments that the proposed model has high accuracy, with the average relative error always less than 1%, which can further improve the effect of quantitative analysis. The proposed model also has a fast convergence speed that can prevent local minima.