Algorithms for Large-Scale Online Training Behavior Data Analysis

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 article aims to study algorithms for
large-scale online training behavior data analysis. 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, and the methods needed to solve the big data
mining task are discussed, and the online course training is recommended in many ways.
Experimental data shows 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%. Through innovative data analysis methods, fast, efficient and timely
analysis of big data streams is realized