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Traditional cigarette quality monitoring and early warning systems often suffer from low automation levels. In this study, we propose a cigarette quality monitoring and early warning system based on microservices architecture to address this issue. The system utilizes the Spring Cloud microservices framework to construct a database for monitoring the cigarette production process. Massive amounts of data regarding the quality of cigarette processing are collected. Deep learning algorithms, specifically the Gated Recurrent Unit (GRU) neural networks, are employed to establish a cigarette quality monitoring model. This approach significantly improves the effectiveness of quality early warnings and enhances the automation level of the cigarette processing process. Through rigorous testing, it has been demonstrated that the cigarette quality monitoring and early warning system, based on microservices architecture and the GRU algorithm, exhibits high flexibility and scalability. This system successfully resolves the problems of low efficiency and difficulty in quality control faced by cigarette production systems in practical applications.