Design And Implementation Of Network Recruitment Data Visualization Analysis System

With the rise of online recruitment methods and the maturity of Internet technology,online recruitment has become the main choice of people when seeking employment,and major recruitment websites have emerged as the times require.China's growing fresh graduates and former graduates constitute a job hunting army,which has attracted employment pressure for today's college students.This article is to use data mining technology to mine the recruitment data of Lagou.com and design a corresponding visual data analysis system to achieve a clear understanding of the current Internet industry recruitment status,which has guiding significance for contemporary college students' job hunting.This article has conducted in-depth research on related theoretical knowledge and technology,also designed and implemented an online recruitment data analysis system based on sufficient needs analysis.The system adopts Python development language and its web framework Django,uses the development platform Pycharm to complete the design and implementation of the system,which mainly includes five functional modules,including data acquisition,data import,data processing,data analysis,and data visualization display.The system first crawls the job information of the Internet industry in Lagou in the data acquisition stage.It uses the web crawler technology during that period.Then it parses the content of the web page through Python's BeautifulSoup library,extracts the job information and saves it in the form of files.After that,the data file is imported into the system through the data import function,therefore the data file is deduplicated,blanked,and the data is unified through the system's data processing function.The job description field is segmented using the Jieba Chinese word segmentation database.It combines the popular stopword lists and custom word lists on the Internet to improve the performance of word segmentation.Then the system analyzes the processed data,first divide the recruitment data by k-means clustering,then get the corresponding hot positions by counting the word frequency,and use the TextRank algorithm to extract the skill keywords for the hot positions,then these recruitment data are used to mine the association rules in Apriori algorithm.Finally,the data is displayedvisually.ECharts chart library and AdminLTE front-end framework are used to display the data in the form of histograms,pie charts and other charts,and their job skills keywords are in the form of weight-keywords and word cloud charts,which can be used to make a presentation.The implementation of the system makes the recruitment data more clear.On the one hand,it helps companies' recruiters to understand the current demand for talents of major enterprises,and on the other hand,it facilitates the timely and targeted adjustment of talent training programs and related courses by universities to cultivate more high-quality talents that meet the needs of today's society.