In order to solve the problems of imperfect fire risk management system and subjective and one-sided risk assessment methods of civil aviation transport aircraft, a comprehensive risk assessment method based on random forest ensemble algorithm was proposed for the analysis and evaluation of risk factor indicators. Firstly, according to the man machine environment management(MMEM) theory, the risk index system was established based on the investigation of the causes of aircraft fire accidents in the past 20 years, and then the scores of scholars and experts in related fields on the correlation between the indicators were collected, and the subjective risk index weight results were obtained by using the analytic network process(ANP) method. The causes of major aircraft fire accidents in the database in the past 20 years were counted and their prior probabilities were calculated by classification, and the Bayesian network(BN) dynamic analysis method was used for reverse reasoning to obtain the probability distribution of each risk factor. The random forest regression model was established to obtain the predicted value and importance of the characteristic index, and put forward scientific and effective suggestions for the fire risk control of the operating unit. The results show that the five indicators of missing dangerous goods in security inspection, failure to eliminate hidden dangers in time, component failure, bird strike, and high surface temperature are the most critical risk factors in aircraft fire accidents.