Evolutionary path analysis and algorithm optimization of industrial economic clusters based on big data mining

By selecting Silicon Valley as a case, this paper analyzes its evolution process from initial establishment to becoming a global center of scientific and technological innovation. Firstly, the research collected and processed multi-source data including economic indicators, technology-related networks, policy environment information and public attention, and used ARIMA model and random forest machine learning model to identify the core factors affecting the development of Silicon Valley science and technology clusters. The results show that the amount of venture capital, the number of patents and government support policies are the key factors in the evolution of Silicon Valley science and technology clusters. Further, this paper puts forward the algorithm optimization strategy, including distributed computing, application of machine learning technology and optimized data preprocessing flow, in order to improve the calculation efficiency and accuracy of big data mining algorithm. Experimental results show that the optimized algorithm is significantly superior to the baseline algorithm in accuracy, recall, F1 score and running time. This study not only provides a profound insight into the evolution mechanism of industrial economic clusters, but also provides scientific decision support for the government and enterprises, and promotes the wider application of big data mining technology in the field of industrial economic analysis.