Research Of Occupation Classification Based On Big Data Analysis ——Taking The Live Salesman As An Example

Under the influence of many factors,the labor market has catalyzed the emergence and differentiation of new occupations.The employment patterns have also tended to diversify,requiring the public sector to do a good job in occupation management.Therefore,our country has gradually established and perfected its occupation classification system to meet the needs of economic and social development and human resource management.However,in the description of occupational standards,there are still defects such as unclear content,slow update,and poor guidance.The problems such as inconsistent occupation classification standards in the labor market and irregular occupation names also hinder the effective implementation of occupation classification management in the labor market,so that the public sector is unable to monitor the development and changes in the number and structure of different occupations,and there is still a lack of reliable hands in the decision-making process.In recent years,the emergence of big data in the labor market represented by job advertisements has expanded the types and sources of data,which can make up for the lack of data in traditional statistical survey methods,and it also more objectively reflects the overall situation of the labor market,creating the conditions for improving the occupation classification.management.Based on the labor division theory and the person-job matching theory,this article takes the live salesperson as an example,discussing the process of constructing professional standards,and on the basis of professional standards,using labor market big data and machine learning technology to explore the realization path of universal Occupation classification prediction.There are two parts in the research.The first phase of the research is to construct the professional standards for live salespersons.The job description just based on the labor division theory can no longer satisfy the objective and detailed description of a certain profession.Skill level descriptions included in the dimension of professional standards can enrich the content of professional standards.Through conducting depth interviews with 25 live salespersons or career-related personnel,and sorting,summarizing,coding,and deducing the interview results,the professional standards of live salesperson have been gradually established.The second phase of the research is to use the labor market big data to predict the Occupation classification of live salespersons based on professional standards.Through the crawler software,we collected 2535 recruitment advertisements of live salespersons,and used the naive bayes text classification selected from machine learning technology to construct a model of occupation classification prediction to predict whether the recruitment advertisements really belong to the occupational category of live salespersons.The effect of classification prediction is evaluated,and the result shows that the model of occupation classification prediction has high accuracy and stability.If it is extended to all occupations,it can realize occupation classification in the labor market based on the current occupation classification system.The research result enriches the existing research in the field of occupation classification management,and deepens the research system of human-job matching theory.It not only expands the dimensions of occupational standards in the content,enhancing the guiding value of occupational standards,but also makes occupation classification management shifting from just a standard to effective implementation and enforcement.At the same time,it also envisages a wealth of application scenarios for the research results.First,the public sector can use the results for employment data collection and employment service matching.Second,we can use professional standards to guide individuals to do their career management.Third,it promotes the establishment of career education curriculum standards and improves the quality and level of corporate training based on the professional standards.Fourth,it provides the assistance for enterprises to optimize the allocation of human resource.