Design And Implementation Of Spark Intelligent Recommendation System Based On Academic Development Platform

With the promotion of the new college entrance examination reform system and the quality education model,the student development guidance education for middle school students has become the focus of attention in the current education field.The student development guidance work mainly solves the three major problems of academic development,college entrance examination,and psychological counseling.This will directly affect the growth of the next generation of the motherland.However,the educational resources for student development guidance are relatively scarce,and the quality of teaching is not guaranteed.If students do not get the help and help of professional teachers in time,it is likely to cause irreparable damage to students.How to combine education and big data technology to support the development of teaching guidance for students is of great research significance.Combined with the analysis of the status quo of students'development work,this paper designs and implements the platform for the development of middle school students,analyzes the behavior data of students in the teaching scene,constructs the student interest preference model,and recommends the educational resources to students individually.The Learning Development Platform collects educational big data through Spark Big Data Platform,builds students'interest preference model,and combines recommendation algorithms to achieve personalized recommendation,so as to enrich the school s education in student development guidance and improve the quality of education.Based on the WeChat public platform development platform,this paper realizes online reservations for students and instructors,online interactive communication,and recommends the latest educational information to students,completes the dataization of teaching scenes,and records and collects big data in the process of education and teach-ing.A collaborative filtering recommendation algorithm based on hybrid self-encoder is proposed to effectively solve the problem of data sparsity and cold start in education scenarios,construct a student s interest preference model,and realize the recommenda-tion of personalized education resources.Combined with Spark big data platform The analysis and mining of educational big data,proposes the recommendation strategy of the hybrid recommendation system,and is responsible for the recommendation service of the academic development platform;completes the deployment of the relevant big data platform,and carries out system testing and maintenance.The results show that the academic development platform can effectively and rationally use the big data under the educational scene,provide students with personalized educational resource recom?mendations,help the school to more comprehensively carry out student development guidance work,and achieve quality education.