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Lung sounds, as an important physiological signal of human body, play a crucial role in the diagnosis and monitoring of respiratory diseases. In recent years, deep learning-based lung sound intelligent recognition technology has made significant progress in the field of medical auxiliary diagnosis, particularly with the deployment of deep learning models on edge devices to achieve local inference and low-latency diagnosis. This has become a key direction in the development of lung sound recognition systems. This paper systematically reviews the fundamental processes of lung sound recognition and analysis, focusing on the construction of lung sound classification models, model lightweight design, and the latest research progress in deploying these models on embedded systems, FPGA, and other hardware platforms. Additionally, the paper looks forward to the application prospects and development trends of this technology in the field of intelligent lung sound recognition. In particular, deployment pathways grounded in hardware–software co-design on embedded and edge platforms are poised to further advance health monitoring systems. This paper provides a comprehensive reference for understanding the key aspects of lung sound recognition technology, from algorithm research to hardware implementation.

Research progress and future prospects in intelligent lung sound diagnosis: models, lightweight design, and hardware platform implementation