基于多片段语义时空图卷积网络的大学生校园日常行为预测

Daily behavior prediction of college students on campus based on multifragment semantic spatiotemporal graph convolutional network

当前大学生校园日常行为预测与挖掘研究中,一般采用统计、聚类、关联关系等浅层挖掘和学习算法,对学生校园行为的时序性、空间位置及其相关性缺乏深层与高阶应用分析。该文基于时空图网络结构,提出考虑校园活动时间序列与层次相关性和空间语义特征相关的多片段语义时空图卷积网络(MFSTGCN)模型。通过构建大学生校园行为数据集并进行实验,该模型达到了90.4%行为预测准确率,优于典型预测模型。最后,以学生个体成长监测为目标,预警日常行为异常的学生;挖掘学生行为习惯等高阶信息,为构建个性化培养提供有意义的参考。

Shallow algorithms of mining and learning such as statistics, clustering, and association relationships are generally used in current research on the prediction and mining of college students’ daily behaviors on campus, and there is a lack of in-depth and high-level analysis of the applications in time series, spatial location, and correlation of students’ behaviors on campus. Based on the network structure of spatio-temporal graphs, this paper proposes a multifragment semantic spatiotemporal graph convolutional network(MFSTGCN) model that considers the time series of campus activities and the correlation between hierarchy and spatial semantic features. By constructing a data set of college students’ campus behaviors and conducting experiments, the model in this paper achieves 90.4% behavior prediction accuracy, which is better than typical prediction models. Finally, we provide students with early warning of abnormal daily behaviors to monitor students’ individual growth and excavate high-level information such as student’s behaviors and habits to provide a meaningful reference for the construction of personalized education.