Abstract: Accurately and scientifically mapping debris flow susceptibility and the determination of key controlling factors and their contribution rates are essential foundations for regional debris flow early warning, forecasting and risk management. The article takes the upper reaches of the Minjiang River as the research area, with small watersheds as evaluation units. Five different machine learning models were employed to construct evaluation models for the susceptibility of debris flows in the upper reaches of the Minjiang River. Quantitative analyses were conducted on the susceptibility of debris flows and the contribution rates of evaluation factors before and after the Wenchuan earthquake. The results indicate that: (1) Integrated machine learning models exhibit higher ACC and AUC values than the shallow machine learning models, with the random forest model performing the best in the assessment of debris flow susceptibility before and after the earthquake; (2) The occurrence rate of debris flow before and after the earthquakes gradually increases with the rise in susceptibility level, and the increment increases with the increase of the level. The occurrence rate of debris flow at all levels is higher after the earthquake than before; (3) The contribution rate of the erosion transmission coefficients before and after the earthquake is significantly higher than that of other factors. This contribution is compounded by the spatial distribution characteristics of the Wenchuan earthquake intensity, further accentuating the spatial distribution pattern of decreasing debris flow development from downstream to upstream in both the main and tributaries following the earthquake.