The development of innovation and entrepreneurship education in higher education institutions has gained significant attention in recent years. This paper aims to propose a design evaluation framework for the development of innovation and entrepreneurship education in universities, based on big data and neural networks. By harnessing the power of big data analytics and advanced neural network algorithms, the proposed framework provides a comprehensive assessment of the effectiveness and impact of innovation and entrepreneurship education programs. Based on the Back Propagation (BP) network’s application concept, this study presents a neural network for assessing the design of university-level innovation and entrepreneurship courses. The major task entails: (1) The ant colony algorithm’s global optimization capability is used to optimize the BP network’s weights and thresholds in light of the characteristics of development design assessment. (2) By integrating both global and local approaches, pheromone updates are accomplished, enhancing the ant colony optimization’s (ACO) capacity for optimization. A function is incorporated into the formula of the worldwide update pheromone in order to modify the information residual coefficient in light of the solution distribution. The local pheromone’s residual coefficient is fine-tuned using a minimum-error-judgment strategy. (3) Construct an IACO-BP network, optimize the BP algorithm using it, and get the best possible weight and threshold choices. Education in innovation and entrepreneurship undergoes a development design review using an optimized BP algorithm.