【摘要】 以提供个性化实验教学资源、提升学生自主学习能力为目标，设计基于大数据分析的高校实验教学平台。利用虚拟计算机节点采集高校实验教学资源，利用Kubestorage云数据存储模块的目录、存储、缓存服务器完成实验教学数据的存储、计算以及节点任务划分和控制，利用时间和共同评分的混合协同过滤推荐算法为用户推荐需要的实验教学资源，通过用户层前端页面接口为用户呈现结果。实验结果表明：该平台可实现数据并发访问，满足多用户登录需求；可根据用户的兴趣点推荐相似度高的实验教学资源，并能够可视化呈现平台数据。更多还原

【Abstract】 In order to provide personalized experimental teaching resources and improve students ′ autonomous learning ability,a college experimental teaching platform based on big data analysis is designed. The virtual computer node is utilized to collect the experimental teaching resources of colleges and universities. The directory,storage and cache server of Kubestorage cloud data storage module is used to store and calculate the experimental teaching data,and divide and control the node tasks.The hybrid collaborative filtering recommendation algorithm of time and common score is used to recommend the experimental teaching resources needed for users. The results are presented to users through the front-end page interface of the user layer. The experimental results show that the platform can realize concurrent data access and meet the needs of multi-user login,the experimental teaching resources with high similarity to the users′ interest points can be recommended,and the platform data can be presented visually.更多还原