Abstract: Data classification is a crucial component in the management of innovation and entrepreneurship project data for vocational college students. However, due to data imbalance, classifiers may suffer from insufficient generalization capabilities, leading to less accurate classification results. To address this issue, this study proposes a data classification method for innovation and entrepreneurship proj⁃
ects. First, textual data from the project database is converted into vector form, and a machine learning classifier based on support vector machines is established. Then, an adaptive synthetic sampling method is employed to oversample the classifier, mitigating data imbalance and optimizing its generalization capabilities. Finally, the transformed project data is classified using the classifier, and the K-means algorithm is applied for clustering integration to achieve project data classification. Experimental results demonstrate that this method achieves high accuracy in classifying innovation and entrepreneurship data, with a mean average precision of 0.986 and an F1 score of 0.973, exhibiting excellent classification performance and promising practical application prospects.

Key words: data classification, college student's innovation and entrepreneurship projects, applied type, project data, higher voca?
tional colleges