Research on Job Market Dynamics Based on Machine Vision and Cloud Computing

In this project, a new algorithm for automatic extraction of visual features of Web user interface is used to minimize human interference and ensure the accuracy of extraction. Through the analysis of the visual characteristics of the Web page, the template of the original Web page of the employment prediction system is obtained. It is then set up manually, resulting in a complete Web page extraction template. Then the thesis makes a comprehensive study of professional job hunting based on Python and Echarts. From the professional job demand distribution, education, experience requirements and the recruitment situation of each city, the information of online recruitment is clearly visualized. The purpose is to allow students to better understand the market needs of professional talents from a large number of job information, so as to clarify their position. In this way, the employment information scattered on the network is converted into a normalized data form and stored. Experiments show that the algorithm has higher accuracy and recall rate, and can obtain better extraction effect.