Research On The Evaluation Model Of Home-School Communication Based On Smart Campus Social Network

With the rapid development of information technology,the concept of smart campus has been concerned widely.Smart campus mainly consists of three parts:home-school communication,smart learning and smart management.The function of the home school communication is the main bridge between the school and the parents,which plays the key role of family education and school education consistency.Therefore,we need an accurate and quick method to analyze and get the corresponding feedback.However,there is a lack of a measure and reference method for the home school communication.Therefore,a system of intelligent campus home school communication assessment method was proposed,divided into three steps:building social network,home network communication dynamics research based on complex network theory,and sorting the key nodes.The first step is to propose a multi-tag classification algorithm based on user identity feature for group chat data processing.Experiments and school deployment practices have proven that through this system approach,a comprehensive assessment and promotion of school home school communication can be facilitated.In the process of building an educational social network,a new multi-label classification algorithm Adaboost.ML algorithm based on user identity was proposed,which adds heuristic rule,and then abandon the concept of time slice,the single piece of information directly as the focus of the judgment,reduce the error due to the edge of the time slice,and finally get the relationship between users.The experimental results show that the false positive rate and the false negative rate are reduced by 53%and 66%respectively compared with the rule-based heuristic algorithm,and also have good classification effect on the WeChat data set.At present,the analysis of the educational social network is less,so complex network theory was used to study the dynamic characteristics of home communication,such as the average degree,the aggregation coefficient,the network density and the change rule of these indicators based on the event situation,for the promotion of home school play an important role.In the network there is a few nodes play an important role,the damage to key node is likely to lead to the collapse of the entire network.so we need to mining and protect them,especially in some practical application scenarios.In this paper,we use Google’s PageRank algorithm to sort the importance of nodes in the background of smart campus social network,and use the robustness and vulnerability index to evaluate the sorting results.The experimental results show that the ranking results of the PageRank algorithm are more in line with the characteristics of educational social network structure than other methods.