Data-driven development of entrepreneurial ecosystem based on the Apriori algorithm

Based on the Apriori algorithm, a data-driven analysis method for entrepreneurial ecosystems is constructed, which solves the problems of serious data silos, static analysis, lack of quantitative analysis, insufficient association rule mining, and difficulty in evaluating implementation effects in traditional research. By collecting and preprocessing data from multiple sources, using the Apriori algorithm to mine frequent item sets and generate association rules, and utilizing data visualization technology to display results, dynamic monitoring and strategy optimization of the entrepreneurial ecosystem have been achieved. The experimental results show that the Apriori algorithm performs well in identifying high-impact item sets and generating high-confidence association rules. The item set support in the entrepreneurial project type A, high investment amount, and experienced background has reached a relatively high proportion, accounting for 40%, 40%, and 60% of their respective sections. In the comparative experiment, the experimental group performed most outstandingly in all evaluation indicators, and the accuracy rate was 4%–23% higher than that of the control group. The significance of this study lies in its contribution to enhancing the depth and breadth of entrepreneurial ecosystem research. By optimizing algorithm performance and expanding the data sources, this article provides a more scientific and comprehensive reference for future studies in this field.