The M_s 7.0 Lushan earthquake triggered a huge number of landslides. Landslide susceptibility mapping is of great importance. Weight of Evidence(Wo E) and Logistic Regression(LR) methods have been widely used for LSM(Landslide Susceptibility Mapping). However, limitations still exist. Wo E is capable of assessing the influence of different classes of each factor, but neglects the correlation between factors. LR is able to analyze the relationship among the factors while it is not capable of evaluating the influence of different classes. This paper proposes a combined method of LR and Wo E for LSM, taking advantage of their individual merits and overcoming their limitations. An inventory of 1289 landslides was used: 70% were random-selected for training and the remaining for validation. 11 landslide condition factors were employed in the model and the result was validated using Receiver Operating Characteristic(ROC) curve. The results showed that the LRWo E model had a better accuracy than the LR model, producing an area below the curve with values of 0.802 success and 0.791 predictive, higher than that of the LR model(0.715 success and 0.722 predictive). It is therefore concluded that the combined method of Wo E and LR can provide a promising level of accuracy for earthquake-induced landslide susceptibility mapping. The M_s 7.0 Lushan earthquake triggered a huge number of landslides. Landslide susceptibility mapping is of great importance. Weight of Evidence (Wo E) and Logistic Regression (LR) methods have been widely used for LSM (Landslide Susceptibility Mapping). However, exist. Wo E is capable of assessing the influence of different classes of each factor, but neglects the correlation between factors. LR is able to analyze the relationship among the factors while it is not capable of evaluating the influence of different classes. a combined method of LR and Wo E for LSM, taking advantage of their individual merits and overcoming their limitations. An inventory of 1289 landslides was used: 70% were random-selected for training and the remaining for validation. 11 landslide condition factors were employed in the model and the result was validated using Receiver Operating Characteristic (ROC) curve. The results showed that the LRWo E model had a better accuracy t han the LR model, producing an area below the curve with values ​​of 0.802 success and 0.791 predictive, higher than that of the LR model (0.715 success and 0.722 predictive). It is therefore concluded that the combined method of Wo E and LR can provide a promising level of accuracy for earthquake-induced landslide susceptibility mapping.