The invention discloses a basic rock temperature prediction method based on an extreme
random forest algorithm, and the method comprises the steps: collecting basic
magma experimental data, constructing a
database containing the experimental temperature and mineral-melt components under a monohectorite-melt
system, and removing unbalanced and extreme experimental data in the
database according to preset conditions, the method comprises the following steps: obtaining an
original data set, carrying out data preprocessing, obtaining an effective
data set, constructing a temperature prediction model based on a limit
random forest algorithm, carrying out preliminary training on the model by using the effective
data set, and determining an optimal
feature combination and a hyper-parameter combination in combination with combined
feature selection and hyper-parameter tuning to obtain a final temperature prediction model. And performing temperature prediction. According to the method, the temperature prediction model which does not depend on the
water content as characteristic input is constructed, the technical problem of measuring the
water content in natural minerals and rocks is effectively avoided, and the method does not depend on prior thermodynamic
hypothesis and has good adaptability and universality.