Factors Affecting Employment Rate Based on Machine Learning

New China’s 75 years of economic and social development achievements in a series of reports mentioned that the current demographic and economic structure by the deep adjustment of China’s labor supply and demand on both sides of the big changes in the field of employment is facing a new situation, the employment of structural contradictions are more prominent. The report of the 20th CPC National Congress puts forward “strengthening the policy of giving priority to employment, improving the mechanism of employment promotion, and promoting high-quality and full employment”, which provides a fundamental guideline for doing a good job in employment. We should adhere to Xi Jinping’s thought of socialism with Chinese characteristics in the new era as a guide, adhere to the implementation of the employment priority strategy, effectively grasp the employment of the people’s livelihood as the top priority, and strive to promote high-quality and full employment, and constantly improve the well-being of the people, in order to comprehensively promote the construction of a strong country with a Chinese-style modernization, and to provide strong support for the national rejuvenation of the great cause. ^[1]However, despite the fact that graduate employment is a top priority nowadays, the industry does not have an accurate predictive method to analyze the main factors affecting the employment rate. This study constructs a machine learning analysis framework based on the random forest model, aiming to systematically identify and quantify the key factors affecting the employment rate of graduates, and study which will have a significant effect on the employment rate of graduates if these factors are scientifically and reasonably adjusted.