Research And Development Of Intelligent Adaptive Learning Platform And Application And Verification Of Classification Algorithms

With the rapid development of deep learning technology,the concept of Internet+education and artificial intelligence +education has attracted more and more people's attention,and a large number of mature machine learning and deep learning algorithms have been applied to the field of education.It is difficult for offline education to truly teach students in accordance with their aptitude.Educational big data and artificial intelligence adaptive learning engines have emerged.This kind of new technology can simulate the tailor-made education program for the special teachers to one-on-one.Greatly improve the efficiency of learning and realize the"private order" of education and teaching.The thesis develops a software system for building a smart adaptation education platform.The system can obtain the student's learning data,analyze the student's learning level,generate a learning report,and initially realize the different levels of learning materials for different levels of students.Planning different learning trajectories,and then providing a basis for the development of post-educational teaching big data and intelligent recommendation algorithms.The main work of the thesis is as follows:1.The characteristics of the existing learning platform are analyzed in detail,and the functional modules and architecture of the intelligent adaptation learning system software are designed based on the actual application objects and scenarios.2.With Alibaba Cloud Centos system as the development platform,combined with multi-threading technology and thread pool technology,MySQL database is used to store data,and python and javascript are used to realize the connection between front and back.The platform is presented to users in the form of online website.3.The platform is based on the React framework,combined with Javascript,CSS,HTML and other technologies.The core programming idea of the component simplifies the code and improves the development efficiency,and realizes the front page and basic functions.4.In this paper,a new TF-IDF(term frequency-inverse document frequency)text feature extraction algorithm is proposed.Based on the original algorithm,the distribution of feature word meaning and feature words is added,and the improved text feature extraction effect of the algorithm is better.The original algorithm has obvious improvement,and the new algorithm is applied to the platform,which realizes the recommended function and text classification function of the platform.