Research And Development Of Intelligent Course Selection System Based On Cloud Computing

The online course selection is an important part of the teaching management system of colleges and universities,which is the key to ensure the normal operation of the teaching work in colleges and universities.The scientific and efficient selection system is the guarantee of work efficiency,teaching quality and talent training.With the continuous advancement of teaching reform in colleges and universities in recent years,many colleges and universities across the country are paying more and more attention to the teaching and management of public elective courses.In the big data environment,college electives tend to be more prone to high-performance concurrency,can support large network traffic access,and rapid response research,while ignoring the rationality and efficiency of student selection.The main research purpose of this thesis is to design a smart course selection system that is convenient,safe,and personalized.Taking Panzhihua University as the research object,this thesis analyzes and draws lessons from other university student elective system.Using JavaEE technology,combined with technology framework Spring MVC multi-component application development technology architecture,and cloud architecture development platform,build a stable,high-performance cloud computing-based intelligent course selection system based on B/S architecture.It is the main research content of this article to realize the curriculum that is most needed for students through intelligent recommendation and improve their enthusiasm for learning so as to achieve a reasonable arrangement of course selection.The cloud platform architecture and related key technologies used in the course selection system were introduced.The problem of “peak pressure congestion” during the course of course selection was highlighted in the elective courses of major universities,and a high-performance cloud-based dynamic resource expansion was proposed(DRX)course selection program,optimizing the performance of the course selection system under high concurrent access conditions.Through the pressure test,it is found that the online course selection system in the large data environment has been optimized to a great extent,which makes the consumption of resources significantly reduced,the efficiency is greatly improved,and the response time is greatly reduced.In addition,the main ideas and characteristics of the main course selection algorithms are analyzed,and the application conditions and advantages and disadvantages of these course selection algorithms are analyzed,and a reasonable algorithm for different courses is carried out by using the hierarchical selection strategy algorithm.Through the analysis and comparison of the intelligent elective course design scheme,the design proposal of intelligent elective course based on BP neural network algorithm was further proposed.The intelligent elective course model was given and validated in the elective course at Panzhihua University.The system acquires the basic information of the current student through an online questionnaire survey,excavates the student's favorite course through past historical data,presents the intelligent recommendation course,and provides certain guidance suggestions during the student's course selection process.Not only makes the course selection process for students more convenient and faster,but also results in a more fair and reasonable course selection,with a certain degree of human characteristics.In the end,using the combination of black box and white box test,each function and sub module of the course selection system of Panzhihua University are tested respectively.Experimental data and experimental conclusions are given to verify the feasibility and effectiveness of the intelligent course selection system based on cloud computing.