The C5.0 decision model is used to study the distribution of vegetation under global climate conditions, providing a theoretical basis for vegetation conservation and coping with the effects of global climate change on vegetation. Based on websites such as WorldClim, CHELSA, and global vegetation distribution datasets, and using ArcGis for meteorological and vegetation data extraction. Subsequently, global meteorological and vegetation data were integrated into SPSS Modeler to build C5.0 prediction model, and model analysis was performed using accuracy, confidence, scatter plot, K-Means, and substituted into future climate data for vegetation prediction, and finally a map of current and future predicted vegetation types was drawn and overlaid with the global administrative map to analyze the global vegetation distribution. The results showed that the accuracy of the C5.0 model prediction was 69.67%, 68.23%, and 72.59% for the training set, test set, and validation set, respectively, which had high accuracy and thus had some reference significance for the study of global vegetation distribution.