An Analysis of Employment in Agricultural and Forestry Colleges Based on Logistic Modelling

Due to the rapid societal development and the impact of the novel coronavirus, the employment rate of graduates from agricultural and forestry colleges has been significantly affected. Considering the specialized nature of talent training in this field and the limited employment prospects, this issue has garnered substantial attention from society. This paper aims to address this concern by utilizing Python to crawl data pertaining to the “employment situation of graduates from agricultural and forestry colleges and universities” from major internet sources. The gathered text information is subsequently categorized using the LDA topic model, visualized through a word cloud map, and analyzed across seven categories: professional competence, social competence, concept of employment, attitude towards employment, intimate relationship, information channels, and discrimination. Additionally, the analysis is reinforced through a questionnaire survey and the implementation of a logistic regression model to explore factors influencing the employment outcomes of China Agricultural University graduates. By examining the significance of the seven key variables, this study establishes their impact on employment and determines their interconnections. Finally, this paper proposes recommendations to alleviate the employment challenges faced by graduates of agricultural and forestry colleges and universities, considering perspectives from society, universities, and individuals.