Design And Implementation Of Cloud Employment Management Platform For Colleges And Universities

At present,the number of college graduates in China is increasing year by year.The government,enterprises and other organizations should guarantee the smooth employment of college students from many aspects such as employment recommendation service.However,due to the rapid development of Internet technology,information explosion growth,the network is filled with massive employment related data,which puts forward new requirements for college employment management platform.Therefore,based on the in-depth analysis of the current demand for cloud employment management platform of colleges and universities,this paper adopts the improved employment quality evaluation algorithm of colleges and universities based on grey theory,and designs and implements the employment management system with three main function modules: "Employment help","Recruitment pool" and "graduates".Firstly,two main problems existing in the existing employment quality evaluation model based on clustering algorithm are analyzed :(1)due to the large number of enterprises receiving graduates each year and the uneven distribution of various conditions of enterprises,the existing model often extracts too few clustering factors.Due to the positive correlation between the quality of clustering results and the number of factors,the quality of clustering results is difficult to be effectively guaranteed in the case of a small number of clustering factors,and the problem of large differences in the results of multiple clustering is easy to occur,or even the subjective factors have a great influence on the results of clustering.(2)In order to pursue the objectivity and authenticity of clustering results,some employment quality evaluation models extract too many clustering factors.These models are also disadvantageous to improve the quality of clustering results.This is because,once there are too many clustering factors,the problem of unclear primary and secondary among various factors will be highlighted.Especially in the big data environment,the extraction of too many clustering factors will reduce the clustering efficiency.Therefore,in view of the limitations of existing models,an improved clustering algorithm is proposed to combine with the classic Employment Quality Evaluation Model EQEM(Employment Quality Evaluation Model,Fca-eqem(Fuzzy Clustering Algorithm Based Employment Quality Evaluation Model,FCA-EQEM).Secondly,in order to further improve the effect of employment quality evaluation model,this paper proposes an improved employment quality evaluation algorithm based on grey theory,and then constructs a new evaluation model t-FCA-EQEM.Before fuzzy clustering,the factors were sorted by grey comprehensive correlation degree based on the similarity level of model variables of geometric shape of sequence curves.And a new evaluation model t-FCA-EQEM is applied to design and implement a cloud employment platform system of a university in Hunan province.Finally,through the system test,it is found that applying the employment quality evaluation model proposed in this paper to the employment management platform system of college graduates can realize the intelligent promotion of employment information;The employment management platform in this paper solves the correlation problem between graduates’ job-hunting intention,job-hunting advantages and job-matching activities.The research results can also provide valuable technical analysis means for both employers and employers,so as to effectively improve the success rate of job hunting and the matching speed of both employers and employers.The simulation results show that the proposed model has better man-job matching accuracy and computational speed than the classical model,and each functional module achieves the desired effect.There are 21 figures,39 tables and 59 references.