Intelligent system for cognitive study on student's network based on computational analysis

Abstract

The neurological, behavioral, and cognitive problems in teenage students are increasing day by day. It is very important to address these issues by gathering information from public opinion. The distribution of Public Opinion Information (POI) in student work is unsystematic, and it is difficult to extract the needed information intelligently. In this research, an intelligent extraction algorithm for analyzing public opinion data from student work is proposed. The algorithm is based on human-computer interaction, machine learning, and computational techniques. A fuzzy semantic autocorrelation mapping feature set of public opinion information is developed from the student work, along with a spatial structural model of data semantic distribution features. The statistical feature quantity of semantic similarity of the public opinion data is retrieved from the student work, and semantic ambiguity is decreased. Adaptive learning and machine understanding, along with human-computer interaction, are used to process the data. In the human-computer interaction machine understanding center, the processor adjusts the grid partition of public opinion information of the student work according to the differences in statistical features, constructs a feature decomposition model of student behavior, and performs context mapping. Finally, semantic analysis is carried out to analyze student behavior based on cognitive study. The simulation outcomes show that the proposed method is computationally inexpensive, has low time complexity, and has the ability to provide real-time monitoring of public opinion information on student behavior.