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At present, in China's automobile manufacturing industry, the main problem is the manufacturing of parts and on-board equipment. Most domestic industries still adopt the step-by-step production of parts, and each manufacturer customizes the required parts according to the scale and production needs of its own enterprise. This situation is easy to cause unstable quality of parts and serious unqualified quality inspection problems. Based on the above situation, we study the high-quality development, parts quality optimization, and remanufacturing of auto parts manufacturing industry with the support of machine learning model. Firstly, based on the analysis of auto parts procurement and production mode, this paper briefly describes the basic problems in the manufacturing process of auto parts in China. Machine learning technology is used to count the changes of quality data in manufacturing, and the quality standard is reflected in the learning model. The machine learning algorithm is used to diagnose and analyze the faults of auto parts and equipment, so as to turn high-quality production to high-quality production. The projection feature extraction algorithm is used to quantitatively analyze the low quality state of automobile parts. Finally, 3D printing technology is used to solve the quality manufacturing problem of parts with high-precision requirements, and the later materials are processed again to achieve the purpose of remanufacturing planning. The results show that the transformation of auto parts manufacturing to high quality can improve the economic development of the auto industry and meet the needs of modern society. The data analysis of parts controlled by machine learning model can help the precision manufacturing of automobile parts. [ABSTRACT FROM AUTHOR]