The Design of University Employment Information Service System Based on Big Data

摘要

At present, there are many problems in the employment information service of colleges and universities, such as students' difficulty in choosing a job, and it is difficult for colleges and universities to obtain feedback from the employment market to guide teaching work. In view of these problems, the university employment information service system based on big data was developed. With the help of big data related technologies, the system recommends personalized work for students to help colleges and universities track the skills needed for employment in time. The system is designed based on the spark technology of the big data parallel computing framework of in-memory computing, it uses Scrapy Web crawler technology to obtain job data from the internet for the system and save it to the MongoDB database. As for the specific system functions, TF-IDF algorithm is used to extract the features of the job data, K-means algorithm is used to cluster the job types, then job recommendation is carried out through text analysis, LDA thematic model algorithm is used to analyze the hot spots of the employment market, and finally achieve job recommendation and analysis of job data.