学生体质健康程度关乎国家的未来，民族的希望，从《中共中央国务院关于加强青少年体育增强青少年体质的意见》（中发〔2007〕7号）、《国务院办公厅转发教育部等部门关于进一步加强学校体育工作若干意见的通知》（国办发〔2012〕53号），到《国务院办公厅关于强化学校体育促进学生身心健康全面发展的意见》（国办发〔2016〕27号），国务院办公厅对于学校体育教育教学方面的通知和意见下发的越来越密集、也越来越迫切。 而大数据在教育领域的应用也越来越广泛,基于教学数据的学习分析在教育大数据背景下应运而生。学习分析是当今炙手可热的研究领域，掀起了教育信息化、智能化的新浪潮，而对于学生体质健康测试数据的分析也属于学习分析的一部分。大数据时代，我国学生体质健康测试数据中蕴含的大量信息亟待深度挖掘,本文通过对数据的梳理、建模和可视分析，清晰、精准地表达出学生体质数据背后潜藏的信息和规律。本文通过对北京市东城区53所学校2016年体质健康监测等数据的收集与整理，运用数理统计法、文献资料法、多维度交互式可视化分析法等，在匹配《国家学生体质健康评价标准》对学生体测成绩进行评估计算后，设计了一套数据清洗和规范化流程，在此基础上研究了影响学生体质分数的因素，提出了一种评价学生体质的计算模型，基于以上内容设计与开发一套可视分析工具，对学生体质健康数据进行探索和解读，探索现阶段不同年龄、不同学校体育干预情况下、不同性别群体间的学生身体形态、身体机能和身体素质状况，揭示各个测试维度间的相关性，有着深刻的现实意义，教育决策者或教师、学生通过对平台的使用，发现数据中隐含的信息，并进一步去研究、探索信息背后深层次的原因，从而为改善和提高小学学生体质健康水平有着指导意义，为有关部门制定政策理论等提供依据。 该系统的意义在于：本文设计的多维度教学数据可视化方法，利用平行坐标、GIS图、年轮图和气泡图对整个年级的高维度的教学数据进行了个体、群体和全局的关联可视分析，并根据该方法开发了一套教学大数据可视分析平台，可以对学生体质测试数据进行探索式分析和可视化解读。

The degree of physical health of students is related to the future of the country and the hope of the nation. From the "Opinions of the Central Committee of the Communist Party of China on Enhancing Juvenile Sports to Strengthen the Physical Fitness of Adolescents" (Zhongfa [2007] No. 7), and the "General Office of the State Council forwards the Ministry of Education and other departments to further strengthen Notice of Certain Opinions on Physical Education in Schools (Guobanfa (2012) No. 53), to "Opinions of the General Office of the State Council on Strengthening Physical Education to Promote the Physical and Mental Health of Students in an All-round Way" (Guobanfa [2016] No.27), Office of the State Council's notices and opinions on physical education teaching in schools have become more and more intensive and urgent. The application of big data in the field of education has become more and more widespread. Learning data based on teaching data has emerged in the context of educational big data. Learning analysis is the hot topic of research today, setting off a new wave of education informatization and intelligence,The analysis of students' physical fitness test data is also part of the learning analysis. In the era of big data, a large amount of information contained in Chinese students' physical fitness test data needs to be dug deeper. We clearly, accurately express the underlying information and laws of students' physical fitness data through combing, modeling, and visual analysis of data. We collect and collate 2016 physical health monitoring data from 53 schools in Dongcheng District, Beijing, and use the mathematical statistics method, literature data method, and multi-dimensional interactive visual analysis to match the National Student Physical Fitness Evaluation Standard. After assessing and calculating the student's physical test scores, a set of data cleansing and standardization procedures were designed. Based on this, the factors affecting the students' physical fitness scores were studied, and a calculation model for evaluating the students' physical fitness was proposed. Based on the above content, the design and development were conducted. A set of visual analysis tools to explore and interpret students’ physical health data, explore physical status, physical function, and physical status of students at different ages and in different school sports interventions and between different gender groups, revealing various test dimensions, It has profound practical significance. The educational decision-makers, teachers and students find the information hidden in the data through the use of the platform, and further study and explore the underlying reasons behind the information, so as to improve and improve the health level of primary school students, and provide the basis for the relevant departments to formulate policies and theories. The significance of this system lies in the multi-dimensional teaching data visualization method designed in this paper, which uses parallel coordinates, GIS maps, annual rings, and bubble charts to perform individual, group, and global visual analysis of high-dimensional teaching data of the entire grade. And according to this method, we have developed a set of teaching big data visual analysis platform, which can carry out exploratory analysis and visual interpretation of student physical fitness test data.