Research on the optimization of college students’ entrepreneurship project based on ant colony algorithm and optimization of EfficientNet deep network

Under the vigorous development of the global digital economy, college students’ entrepreneurship is gradually becoming a new kinetic energy for innovation-driven economic growth. Faced with resource constraints and technical challenges, how to effectively improve the technical competitiveness and market adaptability of entrepreneurial projects is a difficulty that contemporary college entrepreneurs must overcome. This study proposes an innovative idea, integrating the ant colony algorithm and EfficientNet deep network to optimize the critical links of entrepreneurial projects. Experimental data shows that the fusion scheme of this study has achieved remarkable results. In the image recognition task, the model accuracy rate jumped from 89.5% to 93.2%, an increase of 3.7 percentage points, and the model training time was reduced by about 25%. Computing resources were efficiently utilized. Cost–benefit analysis shows that compared with traditional methods, the cost per thousand model tunes is reduced by 18%, providing an essential financial buffer for entrepreneurial teams with tight budgets. The data of this study fully proves the potential of technology integration in improving performance and saving costs and provides a feasible way to optimize college students’ entrepreneurial projects. This study not only enriches the theoretical system of the intersection of deep learning and bionic algorithms but also provides a good idea for optimizing youth entrepreneurial projects.