Prev Med. 2023 Jun 24:107590. doi: 10.1016/j.ypmed.2023.107590. Online ahead of print.

ABSTRACT

With the continuous development of society, people’s life pressure is constantly increasing, and the mental health problems of college students are becoming increasingly prominent, bringing many challenges to their education and management. Universities should not only cultivate students’ theoretical and professional knowledge and practical skills, but also attach importance to their mental health and effectively implement psychological education. Therefore, it is very necessary to develop and design a simple and effective student psychological evaluation system. As a new form of ideological and political transformation in universities in the era of big data, online ideological and political work has potential development space. It is necessary to carry out mental health education in universities, fully utilize online education forms, and improve ability of universities to repair mental health problems. Based on this, this system designs and implements software for typical image resolution based recognition and artificial intelligence. The use of B/S architecture in the development and use of. net technology and web server technology will enable more students to connect and use different terminals. In addition, an algorithm for image super-resolution recognition was proposed, which uses clustering convolution to improve residual blocks, improves modeling ability by extracting features on a larger scale, reduces the number of parameters to improve model calculation efficiency, and enables mental health educators and managers to work better. This article combines image super-resolution recognition technology with artificial intelligence technology to apply it to the process of psychological education in universities, thereby promoting the development of problem repair applications.

PMID:37364796 | DOI:10.1016/j.ypmed.2023.107590