Evaluation and Optimization Path Generation of College Students' Innovation and Entrepreneurship Projects Integrating AIGC Technology

This study aims to integrate AIGC (artificial intelligence generated content) technology to construct a comprehensive model for evaluation and optimization path generation of innovation and entrepreneurship projects. By collecting and preprocessing data from multiple channels such as industry reports, market research data, company financial statements and academic literature, descriptive statistical analysis was conducted to reveal the distribution characteristics of indicators such as market potential, technical feasibility, financial feasibility and risk assessment. On this basis, machine learning algorithms such as linear regression, decision tree and support vector machine were used to evaluate the project, and the superior performance of the evaluation model integrating AIGC technology in terms of accuracy, precision, recall rate and F1 score was proved. Subsequently, an optimization path generation method based on AIGC technology was developed, and the success probability of the project was systematically improved through feature importance analysis, generation of optimization strategies and path optimization. The empirical results show that the success probability of the optimized project is significantly improved, the return on investment is increased, the risk level is reduced, and the market potential and technical feasibility scores are significantly improved, which proves the effectiveness and broad application prospects of this model and method in improving the success rate of innovation and entrepreneurship projects.