An Examination of Changing Views of College Students on Employment and Entrepreneurial Awareness Employing a Modified Decision Tree Classification Algorithm

In the presented study, a method is proposed to effectively address the large-scale, high-dimensional employment and entrepreneurship data of college students and to accurately capture trends in their employment concept changes and entrepreneurial awareness. This approach utilizes an improved decision tree classification algorithm, combined with distributed web crawler technology and ant colony optimization. The web crawler ensures balanced load distribution, while the real-time updating of pheromone levels guides effective crawling. Information relevance is examined against a set threshold for data collection. The collected data are segmented and optimized using the decision tree classification C4.5 algorithm, with shaping to prevent overfitting. In addition, the method utilizes the naive Bayesian theorem to handle missing attribute values and achieve classification. Experimental outcomes demonstrate the method's effectiveness in collecting and classifying college students’ information data, with clear, intuitive analysis outcomes and stable, efficient system performance. The method’s low complexity and fast processing speed further validate its feasibility for classification in line with social realities.