Since the full implementation of the innovation-driven development strategy in the country, all regions and departments have placed great emphasis on the work of mass entrepreneurship and innovation, resulting in a significant increase in innovation and entrepreneurship. The national focus on mass entrepreneurship and innovation has shifted from quantitative to qualitative improvements, with greater attention being paid to improving substance rather than expanding scope. There is a growing trend among colleges to recognize innovation and entrepreneurial management as a significant achievement in college education and teaching. By prioritizing innovation and entrepreneurship management and professional education, the aim is to cultivate college students with an entrepreneurial spirit, and to develop inventive and entrepreneurial talent. Cultivating innovative talent is one way to apply the innovation-driven development plan and build a creative country. However, the implementation of the innovation and entrepreneurship development plan in the country has posed both new opportunities and challenges to the management of innovation and entrepreneurship education in colleges across the country. As a result, innovation and entrepreneurship have become increasingly important tasks that can help improve the management of innovation and entrepreneurship in these institutions. In order to address this issue, this paper proposes a neural network for evaluating innovation and entrepreneurship management methods in colleges, which combines convolutional neural networks with these tasks. Specifically, a multi-scale convolutional neural network is designed to more efficiently extract innovation and entrepreneurship management features in colleges, ultimately leading to improved model performance.