Application of Network Score System for Sports Achievements The management of sports scores of college students is a work that is heavy, complicated and errorprone. This paper applies the sports score network scoring system, and the three scoring items of student selfassessment, student mutual evaluation, and solidarity evaluation are completed by the network. This improves the level of participation and privacy of the students and ensures that the scores are comprehensive and objective. The teacherxe2x80x99s grade entry is also completed by the network, and the total score evaluation and uploading of the Academic Affairs Office are automatically completed by the system. The results show that the application of the network scoring system makes the management of sports performance more accurate and reliable.

Evaluation of Physical Education Teaching Quality in Colleges Based on the Hybrid Technology of Data Mining and Hidden Markov Model In order to further improve the teaching quality evaluation accuracy of physical education(abbreviated as PE) curriculum in colleges, this study conducts an indeep research on the overall evaluation of PE teaching effect in colleges from the aspects of teachersxe2x80x99 teaching ability and studentsxe2x80x99 learning effect based on the hybrid technology of data mining and hidden Markov model. First of all, this study analyzes the development status of the teaching quality evaluation system of PE curriculum in colleges; Secondly, it analyzes the applicability of data mining technology and hidden Markov model to the evaluation of PE teaching quality in colleges, and proposes a mathematical model for evaluating the quality of PE teaching in colleges; Finally, this study carries out a series of experiments on the basis of mathematical models, and analyzes the experimental results in depth. The experimental analysis shows that the model proposed in this paper is helpful to improve the accuracy of PE teaching quality evaluation in colleges. The research results of this study provide a useful exploration for the integration of computing technology and language teaching. At the same time, it provides a reference path and implementation model for improving the teaching of PE for graduates in colleges through machine learning technology.