Optimization of artificial intelligence supporting the ecological construction of college students’ innovation and entrepreneurship

Against the backdrop of urgent demand for efficient scheduling and precise services in university innovation and entrepreneurship ecosystems, this study proposes an optimization path for ecosystem construction integrating Artificial Intelligence (AI) methods. It focuses on three core technologies-recommendation systems, data modeling, and health diagnosis, and builds a multi-module collaborative model to empower student innovation and entrepreneurship practices. Based on operational data from two universities, the study adopts normalization processing and a controlled experimental design to dynamically evaluate the evolution of ecosystem indicators under intelligent intervention. Experimental results show that, after AI optimization, the recommendation system in the innovation and entrepreneurship ecosystem achieves a Precision@10 of 0.782 and a Recall@10 of 0.746, while the click-through rate increases to 14.9%. The comprehensive ecosystem health index of the experimental group reaches 0.77, significantly higher than the control group’s 0.56, with all key dimensions showing steady growth. Multidimensional evaluation metrics indicate that the adoption rate of recommended resources rises to 0.79, and the platform rating reaches 0.81. Both entrepreneurship project success and user retention continue to improve, reflecting the system’s strong effect on promoting efficient resource integration and user behavior guidance. The research findings indicate that under the experimental conditions covering two universities over a one-year period, AI technology has exerted a positive impact on the innovation and entrepreneurship practices within complex educational ecosystems. This study provides a referable theoretical and technical pathway for universities to explore the optimization of innovation and entrepreneurship ecosystems. It focuses on three core technologies-recommendation systems, data modeling, and health diagnosis. The study adopts normalization processing and a controlled experimental design to evaluate the evolution of ecosystem indicators. Both entrepreneurship project success and user retention continue to improve.