Design And Implementation Of Information Management And Employment Recommendation System For College Students

The number of new graduates has hit record highs every year since 2010,while the employment rate has continued to decline since 2015.The employment situation of recent graduates has become more and more serious.At the same time,the employment situation of enterprises is also rigorous.Students can't find their favorite jobs.Enterprises can't recruit suitable talents.There is huge information gap in the middle of the current level.Under the premise of comprehensively popularizing the electronic informationization of student information management,colleges and universities use artificial intelligence technology to analyze and predict student information,and provide reliable employment positioning reference,which can effectively alleviate the current employment tension.Student information management is one of the main daily management tasks of universities.It involves many aspects of the school,department,division,and students.With the continuous reform of the teaching system,especially the credit system and the elective course system,the students' daily performance management work and preservation management are becoming more and more complicated.At the same time,with the changing employment situation,the national economic transformation and the adjustment of domestic industrial structure,the demand of emerging enterprises is rising,the demand of traditional enterprises is fixed,and the forecasting system for student employment analysis In the employment work of colleges and universities began to emerge,and with the rapid development of foreign neural network technology,the value and role of big data and cloud computing began to manifest.Therefore,we designed a student information management system by using Java,SQL and python.We first developed a web framework of the management system,we then designed a job recommendation system by using Python.Our original data and generated recommendation data were both stored in our database.We also implemented a web application which can fetch and analyze original student performance data as well as recommendation results.