Path identification and effect assessment of digital economy-driven manufacturing quality development in the context of big data analysis

This paper uses big data analysis technology to construct a digital intelligent manufacturing system. Firstly, the K-mean algorithm is used to cluster the enterprise manufacturing data, and then the fuzzy C-mean algorithm is combined to detect the abnormal data and realize the preferential selection and control of product features. A semi-parametric algorithm is introduced to establish index weights to achieve optimal resource allocation. The results show that after manufacturing enterprises produce through the digital intelligent manufacturing system, qualified products account for 82% of the total output and productivity increases by approximately 44% on average. Big data analysis technology enables enterprises to analyze data effectively and enhances the development of the manufacturing industry in the digital economy.