In China

low-rank coal and coalbed methane resources are abundant

meanwhile

as a kind of clean energy

the development and utilization of coalbed methane（CBM） can effectively alleviate the shortage of natural gas resources

but the commercial scale development is slightly insufficient

and systematic research is urgently needed. The premise of efficient CBM development is the selection of favorable areas

but the current CBM development evaluation involves certain subjective human factors

which will indirectly affect or interfere with the prediction effect. Taking the low-rank coal in the Dafosi minefield in the Binchang mining area of Huanglong Coal Field as the research object

based on the actual production data

the random forest algorithm in machine learning is used to predict the favorable area of coalbed methane in the area. The results show that: ① Pearson correlation analysis shows that the gas content

ash content

net thickness of coal seam

structural position

roof thickness

permeability

reservoir pressure and burial depth are eight mutually independent CBM output-related parameters and can be used for model establishment; ② The Random Forest algorithm divides the CBM development area into five types of areas with different degrees

of which type Ⅰ（extremely high） to Ⅱ（highly favorable） areas account for 13.88 % of the entire study area

mainly distributed in the middle of the well field. The southeast is not suitable for subsequent deployment of well locations

and there is a distribution of highly favorable areas in the west

so the well locations for subsequent development and deployment should also be considered. ③ It can be obtained from the receiver operating characteristic（ROC） curve

and the area under the ROC curve （AUC） is 0.961

indicating that the Random Forest model has high prediction accuracy and reliable results. Using machine learning algorithms for comprehensive prediction of CBM favorable areas can avoid human subjective factors in traditional algorithms

and can provide a certain theoretical reference for subsequent unconventional oil and gas development and selection.