Respiratory diseases are the third leading cause of death in the world. In major surgeries and endotracheal intubation ventilation therapies, pulmonary and cardiogenic complications are two primary causes of rising medical costs and mortality increasing, but the proper evaluation and treatment of lung diseases has been a long-term debated topic, and also a difficult problem between the cost of labor and the progression of disease. As a motivation to solve the problem, this study combined clinical experience and opinions to develop a lung sound auscultation system capable of continuous monitoring and recording, which achieves intensive continuous monitoring and meets the clinical staff's workflow at the same time. This research put the finished system and similar competing products in the acoustic test to verify its audio performance, and designed a proper clinical lung sound recording method from the clinical auscultation operation in operation rooms and intensive care units. The data recorded by this system proved to be more suitable for either machine learning or big data analysis in clinical lung sound data, and the result can be expected to help improve the quality of medical care.