Under the background of the development of higher education, according to the characteristics of college student management, after analyzing its background and practical significance, this study constructs an intelligent college student management system of Internet of things based on machine learning. The data volume of the Internet of things is huge, so ensuring the normal and efficient operation of the system is the primary goal. In this study, the data management model of the system is constructed with the help of cyclic neural network in machine learning algorithm to predict the data and optimize the computer program. At the same time, the system data are filled and classified by the k-nearest neighbor model, and the data are trained and simulated by constructing a safe bilstm neural network system. Because the information related to students in the university database involves personal privacy, in order to ensure the security of the system and avoid relevant data leakage, in the judgment standard of configuration error data flow, this study calculates the monitoring abnormal data and loss function through the dark network flow and ip2vec algorithm, so as to establish the system abnormal monitoring model and identify the system error data flow. Finally, it constructs the college student management system and expounds on the basic requirements of the system use cases. After a series of tests of system performance, capacity, and stability, the results meet the basic requirements of system operation, which provides a certain reference for the application of college student management in the future.

Background Medical universities use their websites to teach, research, and promote a culture of health. Therefore, this study aimed to evaluate the performance of medical universities in terms of health information and education regarding COVID-19 by surveying the website of Iranian medical universities. Methods This descriptive-analytical study was conducted in June to August 2020 on the websites of medical universities in three categories of universities (type 1, type 2 and type 3). The information of this study was collected from medical universities located in the east, west, north, south and center of Iran. Data were collected according to a checklist. The checklist contained 3 sections; the first part with 8 components regarding general information of the university websites, the second part with 11 components regarding the information and news related to the coronavirus and the third part with 12 components regarding the content of personal health education and environmental health for the prevention of coronavirus. To determine the status of each website in the two areas of health information and education, websites were divided into three categories based on scores (poor, average and good). Data were analyzed by chi-square. Results In this study, 1118 web pages related to 48 Iranian universities of medical sciences were reviewed, where 19 were type 1 universities, 21 type 2 universities, and 8 type 3 universities. The mean scores of the websites regarding the information and news related to the coronavirus (8.54 ± 1.750) and the mean scores of the websites regarding the personal and environmental health education related to coronavirus (10.96 ± 1.148) were in a favorable and positive condition. The ranking of medical universities by type showed that the scores in the two areas of health information and education about the coronavirus were in good condition and none of the universities were in bad condition. Chi-square showed that the information status and news related to the coronavirus had a significantly positive relationship with the type of medical universities (χ2 = 10.343, p = 0.006). Conclusions The results of this study showed that type1 and type 2 and 62.5% of type 3 medical universities were in good condition in terms of total scores in the two areas of health information and education about coronavirus and none of the universities were in a bad situation. It is suggested that the website of medical universities can serve as a reliable and appropriate source of information not only for academics and students but also for the general public.