Intelligent Decision System for Entrepreneurship Based on Big Data Analysis

In the current field of entrepreneurship education, the lag and inefficiency of informatization and data-driven management are gradually becoming apparent. Therefore, this article adopts an intelligent decision system based on big data analysis to optimize the teaching mode and resource allocation of entrepreneurship education in universities. In response to the problem of insufficient data acquisition and analysis capabilities in the current teaching process, the system utilizes computer technology to integrate multiple data mining techniques and machine learning algorithms to achieve comprehensive perception and deep analysis of educational data. Firstly, an entrepreneurship education data perception network is built, integrating multiple data sources such as students' learning behaviors, teaching content, and social media feedback. Secondly, deep learning models such as convolutional neural networks and long short-term memory networks are used to extract learning feature signals at different scales. Then, based on methods such as decision tree algorithm, support vector machine, and random forest, a multimodal joint prediction model is established to achieve personalized learning path recommendation and educational effect prediction. In the dimension of "system performance", the proportions of 4 and 5 scores are relatively high, at 40.5% and 33.5% respectively, which further verifies the high evaluation of system performance by users. The studied intelligent decision system can significantly improve the teaching effectiveness and resource utilization of entrepreneurship education, effectively supporting education managers to make precise educational decisions and promoting the digital transformation and upgrading of entrepreneurship education in universities.