Metabolic dysfunction-associated fatty liver disease （MAFLD） has become one of the most prevalent chronic liver diseases worldwide， posing a serious challenge to public health. In this context， the integration of artificial intelligence （AI）， especially intelligent diagnosis and treatment and digital health interventions based on machine learning， can break through the limitations of traditional methods， realize efficient screening of multi-dimensional data such as key genes， biomarkers， and biochemical metabolites， and achieve revolutionary breakthroughs in risk prediction， subtype identification， and therapeutic effect assessment for MAFLD. This article systematically reviews the ground-breaking application of machine learning models in driving the innovation of clinical diagnosis and precise risk prediction of MAFLD， conducts a comprehensive comparative analysis of digital health practice cases of MAFLD in China and globally， and deeply analyzes their advantages and limitations in terms of research subjects， interventions， and management team. Studies have shown that the deep integration of digital health and long-term management of MAFLD is becoming the key engine driving the transformation of disease management modes towards an intelligent， individualized， and precise era， but there are various ethical and technical issues that need to be addressed urgently.

Objective:To study the optimum ethanol extraction process. Methods: The extraction rate of extractum and total flavonoids as well scutellarin was choosen as the assessment index. The optimum ethanol extraction process was selected with the orthogonal design. Results: The optimum ethanol extraction process conditions follow as: adding 10 fold of 75% ethanol, extracting for 2.0h and twice in all. Conclusion: The average extraction rate of extractum and total flavonoids as well scutellarin was 13.6%, 91.7%, 78.4%(n=3), respectively.