Big Data Model of Higher Education Online Teaching Based on Intelligent Algorithm

Abstract

With the continuous development of society, the demand for processing large-scale data in many fields is increasing. Traditional processing training techniques have many limitations for big data analysis applications. Therefore, how to transform big data into general-purpose information becomes particularly important. This research mainly discusses the big data model analysis of higher education online teaching based on intelligent algorithms. The process of the experiment is to access how trainers interact or receive information stimulation in videos and courseware and how to cause relatively lasting changes in cognitive behavior. From the experimental research, we discovered the law of practical training and finally provided personalized teaching support services according to the needs and abilities of the trainers. On the other hand, the online training algorithm for big data analysis is studied, the methods needed to solve the big data mining task are discussed, and the online course training is recommended in many ways. Experimental data show that the algorithm of large-scale online training behavior data analysis on the behavior analysis results of online trainers is conducive to the improvement of online trainers’ learning efficiency. The experimental results show that the algorithm of large-scale online training behavior data analysis can show good model analysis performance, which is conducive to the prediction of the training personnel, and the prediction accuracy reaches about 90%. It is found that the algorithm that implements large-scale online training behavior data analysis can effectively categorize the relationship between the trainee’s visits. Through innovative data analysis methods, fast, efficient, and timely analysis of big data streams is realized.