Research on the strategy of artificial intelligence in the allocation and decision optimization of innovation and entrepreneurship resources in higher vocational colleges

This study proposes an artificial intelligence–based decision-support framework to optimize innovation and entrepreneurship resource allocation in higher vocational colleges by improving institutional success prediction accuracy. Traditional allocation approaches are largely experience-based and static, lacking objective, data-driven evaluation mechanisms. An Artificial Fish Swarm Algorithm–Enhanced K-Nearest Neighbors (AFSA-EKNN) model is developed by integrating swarm-based parameter optimization with recursive feature elimination, Z-score normalization, and 10-fold cross-validation to enhance prediction robustness and adaptability. Experiments were conducted using a Kaggle dataset comprising aggregated institutional records from 500 higher vocational colleges, including financial, operational, and entrepreneurship-related indicators. The proposed AFSA-EKNN framework achieved a prediction accuracy of 93.0% with a cross-validation loss of 0.070. Feature importance analysis identified annual budget and resource allocation as the most influential factors affecting entrepreneurial success. The proposed framework provides effective data-driven decision support for higher vocational institutions, enabling objective resource allocation, reducing subjective bias, and improving the overall effectiveness of innovation and entrepreneurship education.