With the increase in inclement weather conditions, many countries would experience more and more landslide hazards in the process of planning, designing and construction for engineering projects, especially in the mountainous regions. How to quickly and accurately assess potential landslide risk in a large region (> 10,000 km2) is facing challenge due to its complex geological conditions and large amount of landslides in the region. To optimize the accuracy of the existing models for a large region, in this study, the genetic algorithm (GA) and particle swarm optimization (PSO) are, respectively, coupled with the backpropagation (BP) neural network to determine the initial weights and thresholds in the BP neural network, which can be called GA-BP model and PSO-BP model. To show the reliability and accuracy of the new models in large region, the BP, GA-BP and PSO-BP models are evaluated based on root mean square error (RMSE), coefficient of determination (R2), Kappa coefficient (k), receiver operating characteristic (ROC), training time and condition factor weights by using 100 landslide samples from Sichuan Province, China. Results show that the RMSE values of the GA-BP model and the PSO model are, respectively, 22.6% and 5.1% lower than those of the BP model; the R2 values of the GA-BP model and the PSO model are, respectively, 24.9% and 6.2% higher than those of the BP model; the k values of the GA-BP model and the PSO model are, respectively, 44.3% and 15.4% higher than those of the BP model, and the areas under ROC of the GA-BP model and the PSO model are, respectively, 32.4% and 9.6% larger than those of the BP model. The GA-BP model and the PSO-BP model have better accuracy in the assessment of the overall risk value and the risk-level classification. The difference of the training time is small, and the sequences of condition factor weights given by the three models are consistent. In general, the GA-BP model is more effective for landslide risk assessment in large region. At last, this study gives proposed models under different engineering conditions, which can increase efficiency of the risk assessment for landslides.