A method for selecting influence indicators by using an automatic mechanism, adapted to an electronic device and comprising: obtaining raw data, wherein the raw data comprises a body-related variable and a plurality of indicators corresponding to the body-related variable, wherein the body-related variable comprises a grip strength, a lean body mass, an appendicular skeletal muscle mass, or a relative appendicular skeletal muscle mass (RASM), and wherein the plurality of indicators comprise a plurality of proteins; setting the body-related variable as a target parameter; inputting the body-related variable and the indicators into a plurality of different validation models to predict importance of the indicators relative to the body-related variable by using each of the different validation models, obtaining a plurality of rankings of the importance of the indicators relative to the body-related variable predicted by each of the different validation models, and sorting, according to the rankings, the indicators to obtain ranking data, wherein each of the different validation models is established by using different methods comprising a regression method, a tree-based method or a neural network method; in the ranking data, selecting, as a candidate indicator, the indicators whose ranking is in front of a preset ranking, highest among the ranking data, or correlation coefficient relative to the body-related variable is greater than a preset threshold; calculating a correlation between the candidate indicator and the body-related variable to determine an influence indicator; randomly selecting a plurality of sets of sub-data from the raw data, and inputting the sub-data into the different validation models to obtain a plurality of ranking data according to an output result of the different validation models; calculating an average characteristic number of each of the indicators according to the number of times of selecting the indicator as the candidate indicator in the ranking data corresponding to the sub-data; determining a weight of the different validation models according to a reciprocal of the average characteristic number; and determining the importance of the indicators according to the weight of the different validation models. obtaining raw data, wherein the raw data comprises a body-related variable and a plurality of indicators corresponding to the body-related variable, wherein the body-related variable comprises a grip strength, a lean body mass, an appendicular skeletal muscle mass, or a relative appendicular skeletal muscle mass (RASM), and wherein the plurality of indicators comprise a plurality of proteins; setting the body-related variable as a target parameter; inputting the body-related variable and the indicators into a plurality of different validation models to predict importance of the indicators relative to the body-related variable by using each of the different validation models, obtaining a plurality of rankings of the importance of the indicators relative to the body-related variable predicted by each of the different validation models, and sorting, according to the rankings, the indicators to obtain ranking data, wherein each of the different validation models is established by using different methods comprising a regression method, a tree-based method or a neural network method; in the ranking data, selecting, as a candidate indicator, the indicators whose ranking is in front of a preset ranking, highest among the ranking data, or correlation coefficient relative to the body-related variable is greater than a preset threshold; calculating a correlation between the candidate indicator and the body-related variable to determine an influence indicator; randomly selecting a plurality of sets of sub-data from the raw data, and inputting the sub-data into the different validation models to obtain a plurality of ranking data according to an output result of the different validation models; calculating an average characteristic number of each of the indicators according to the number of times of selecting the indicator as the candidate indicator in the ranking data corresponding to the sub-data; determining a weight of the different validation models according to a reciprocal of the average characteristic number; and determining the importance of the indicators according to the weight of the different validation models.