The essence of production quality process control is to organize effective production, accurately identify and manage potential quality risks in the product manufacturing process. And constantly improve the driving force of self-improvement, which is a process of continuous improvement of the management system. However, due to the management guided by the production and manufacturing results. And the potential quality risks have not been unaware, which makes us ignore the control before and during the event, resulting in frequent production quality accidents. Managers cannot accurately judge the operation status of quality control, and the process improvement drive is weak. Artificial intelligence technology can bolster quality control in the production process. It effectively identifies potential risks that arise from various issues. These issues include inaccuracies in production organization and uncoordinated process sequences. It also addresses the risks from delayed information transmission and unclearly defined powers and responsibilities. Additionally, it deals with the use of unreasonable analysis tools. This technology helps to eliminate major and extraordinary production quality accidents. Ultimately, it contributes to achieving transparent production. This chapter introduces the knowledge management method of data organization in the production process and finally gives the intelligent control and energy consumption control methods for spinning production.