Learning has been a significant emerging field for several decades since it is a great determinant of the world’s civilization and evolution, having a significant impact on both individuals and communities. In general, improving the existing learning activities has a great influence on the global literacy rates. The assessment technique is one of the most important activities in education since it is the major method for evaluating students during their studies. In the new era of higher education, it is clearly stipulated that the administration of higher education should develop an intelligent diversified teaching evaluation model which can assist the performance of students’ physical education activities and grades and pay attention to the development of students’ personalities and potential. Keeping the importance of an intelligent model for physical education, this paper uses factor analysis and an improved random forest algorithm to reduce the dimensions of students’ multidisciplinary achievements in physical education into a few typical factors which help to improve the performance of the students. According to the scores of students at each factor level, the proposed system can more comprehensively evaluate the students’ achievements. In the empirical teaching research of students’ grade evaluation, the improved iterative random forest algorithm is used for the first time. The automatic evaluation of students’ grades is achieved based on the students’ grades in various disciplines and the number of factors indicating the students’ performance. In a series of experiments the performance of the proposed improved random forest algorithm was compared with the other machine learning models. The experimental results show that the performance of the proposed model was better than the other machine learning models by attaining the accuracy of 88.55%, precision of 88.21%, recall of 95.86%, and f1-score of 0.9187. The implementation of the proposed system is anticipated to be very helpful for the physical education system.

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The centralized database can store a variety of electronic archives. Electronic archives face a variety of network attack vulnerabilities as information technology and network technology continue to advance. Furthermore, these archives will be readily forged and tampered with by internal management or external attackers. Data security and authenticity problems prevail in China’s management system for archives. First, this exploration elaborates the blockchain technology, distributed database technology, and distributed database system structure. Secondly, blockchain technology is applied to the authenticity protection of electronic archives. Then, an optimization model of university archives based on blockchain technology is constructed. Finally, this exploration investigates the current use of blockchain technology for college archive management systems. The questionnaire is used to understand the current university personnel’s views on the college archive management system under the application of blockchain. The survey results suggest that most people support the digital college archive management system. At present, the operation efficiency of the college archive management system still needs to be improved, and the quality of archives search should be promoted. Therefore, the college archive management system still needs to optimize the archive’s efficiency as well as quality. According to the above survey results, this investigation gives suggestions for optimizing the college archive management system using blockchain technology, as well as some suggestions and references for further management.

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