The application of high-performance computing technology in product quality testing can improve enterprise productivity and product quality, creating conditions for the development of new quality productivity, which needs to be supported and guided by economic policy innovation. This study explores the potential of highperformance computing technology, especially the application of random forest model in product quality inspection for the improvement of new quality productivity and economic policy innovation. By constructing a product quality inspection system based on the random forest model and applying it to enterprise production practice, the impact of high-performance computing on enterprise productivity and economic efficiency is analyzed. The study uses a combination of experimental validation and enterprise case study to comparatively analyze the changes in the economic indicators of enterprises before and after the application of the technology. The results show that the product quality inspection system based on the random forest model shortens the average beat time of the enterprise by 28.56%, and the fault prediction accuracy rate reaches 98.54%; after the implementation of highperformance computing technology in enterprise A, the return on net assets in 2024 is 16.97% higher than the industry average, and the inventory turnover rate increases to 10.13 times. Based on the empirical analysis, this paper proposes three economic policy innovation paths, namely, optimizing the design of tax incentives, adjusting the structure of fiscal science and technology investment and enhancing the synergy of industrial support policies, in order to support the application of high-performance computing technology in a wider range of fields and promote the development of new-quality productive forces, which provides a theoretical basis for the formulation of relevant economic policies.
