Research And Design Of Classification In Social Learning Network

With the development of Internet technology,the society has entered the era of information explosion,and people's increasing demand for knowledge has been beyond the scope of traditional education mode.How to make full use of information resources in the information world,and create a personalized learning environment to meet the needs of people learning anytime and anywhere,has become a top priority.Social learning network is used for discovery,integration,storage and dissemination of knowledge based on Web2.0 technology and large data mining technology.It can provide personalized learning programs according to the information of user,and provides users with knowledge by human computer interaction way such as text,voice,video to meet the needs of people learning anytime and anywhere.Social learning network can meet people's demand for learning in the era of information explosion,but because it is in the initial stage of development,the intelligent mechanism of social learning network is far from perfect.Based on the development of mobile Internet and big data mining technology,this paper studies the design and application of classification methods in the social learning network,and the main research work and innovation points include the following aspects:1)In order to obtain reliable data,this paper firstly designs and develops an online learning platform.Different from the existing online learning platform,the platform not only provides the course discussion,teaching video playback and other learning functions,but also provides information guidance,campus interaction and other social functions.It provides a reference for the design and implementation of platform for social learning network.As a platform for classification,the platform realizes the function of data collection in social learning network.2)The course discussion area of the social learning network is used for the communication between users,but at present,there is a phenomenon of information overflow and disorder,which brings great inconvenience to users.Aiming at this problem,this paper uses data mining technology to put forward a method of information classification and ranking,which can help users to get useful information quickly.Different from the existing method of single course classification,the paper uses the topic information of N courses to form the data source,and converts the two meta unbalanced classification problem into n+1 meta balanced classification,so as to improve the accuracy of topic classification and ranking.The experimental results verify the effectiveness of the proposed topic classification and sorting algorithm,which improves the intelligent mechanism of social learning network.3)Social learning network saves a lot of video clicking information from users,but the information is not fully used,resulting in a great waste of information.To solve this problem,this paper proposes a method of user classification based on video click stream data,which uses the user's click behavior to identify the degree of the user's learning.Different from the existing research on video click behavior,the paper improves design of the user click event,establishes a new user clicks model,and connects user's behavior with user's learning based on online learning platform.The experimental results verify the accuracy of the proposed.video learning user classification algorithm,which improves the personalized strategy of social learning network.