This study aims to investigate the relationship between forest fire occurrences and their driving factors in the Qinling Mountains from 2003 to 2018 and to predict the probability of forest fires Based on MODIS satellite fire point data from 2003 to 2018 logistic regression random forest and support vector machine models were developed to predict forest fire occurrences in the Qinling Mountains and their accuracy was evaluated The random forest model which provided better fitting results was chosen to create the forest fire risk level map The results showed that 1 The random forest model had better fitting results for predicting forest fire occurrences with an accuracy of 87 04% and an AUC value of 0 949 2 The importance of the driving factors for forest fire occurrences in the Qinling Mountains in descending order were NDVI average monthly wind speed elevation average monthly atmospheric pressure wind speed atmospheric pressure average monthly precipitation slope average monthly air humidity air humidity con secutive days without precipitation and precipitation amount 3 The eastern and southern parts of the Qinling Mountains were identified as areas prone to forest fires It is recommended to increase forest fire prevention publicity in these counties to reduce human-caused fires and to enhance infrastructure such as lookout towers to ensure early detection and early suppression of forest fires