With the rapid development of manufacturing industry, product quality testing has become a key link to ensure product performance and customer satisfaction. However, traditional quality inspection methods often rely on manual testing, which has problems such as low detection efficiency and high misjudgment rate. To address these issues, this paper proposes a quality inspection system based on artificial intelligence. Firstly, by analyzing the pain points and requirements in the quality inspection process, the design concept of the quality inspection system is proposed. Secondly, the workflow and key technologies of the system were detailed, such as data acquisition, image processing, and defect recognition. With the support of deep learning algorithms, the system is able to extract features and recognize patterns from detection data, thereby achieving automated quality inspection. Finally, conduct experimental evaluation on the quality inspection system. The results indicate that the system has high detection accuracy and efficiency, and can effectively improve the automation level of the quality inspection process.