Innovation and entrepreneurship education guidance and optimization analysis based on deep learning optimization algorithm

According to the constructivist learning theory, learners need to actively construct a knowledge system in a practical situation, and deep learning, with intelligent data analysis, pattern recognition and predictive decision-making capabilities, just builds a practical platform for students to discover market opportunities and formulate innovative solutions, which effectively promotes the development of their innovation and entrepreneurship skills. At the same time, deep learning emphasizes the understanding, utilization and generation of knowledge, and further strengthens students’ innovation and entrepreneurship literacy by cultivating human-machine collaboration, which is in line with the concept of “learning by doing” in contextual cognitive theory. In addition, deep learning has the characteristics of thinking training, knowledge transfer and application, which echoes the requirements of higher-order thinking cultivation in Bloom’s educational goal classification theory, which can effectively improve the knowledge integration, transformation and application ability of college students, and enhance the ability of knowledge creation and innovation and entrepreneurship. In the application of practical education scenarios, taking the YOLOv5s network as an example, after the introduction of the SE module and the SIoU loss function, the average accuracy (mAP) of the feature detection and entrepreneurship scene recognition tasks of innovative projects is increased to 89.74% and 89.33%, respectively. This significant performance improvement intuitively demonstrates the ability of deep learning algorithms to accurately analyze education data. Through the efficient processing of educational data such as classroom teaching videos and project display images, the system can accurately identify students’ innovative thinking performance and entrepreneurial practice scenarios, and then provide data support for teachers to optimize teaching design and reform teaching methods, tailor personalized learning paths for students, truly realize data-driven educational innovation, and promote the deep integration of innovation and entrepreneurship education theory and practice.