We investigated the soil erodibility (K) of red soil in Guangdong Province and established its predication model based on field artificial rainfall events and indoor measurement of soil physical and chemical indexes. There was a significant correlation between K and clay, sand and organic matter content (P < 0.01), with Pearson’s correlation coefficient of - 0.920 4, 0.925 9 and - 0.6424,respectively. The stepwise regression analysis (SRA) filtrated the single factors and interacted terms,which significantly affected K. The interacted influence between the silt and organic content was the largest. The SRA prediction model for K had a good estimation, and the relative error between predicted and observed values was less than 25%. Using the single factors and interacted terms filtrated by SRA as the input parameter, and K data as the output parameter, we established the BP predication model for K. The grey relational analysis indicated that the optimal network structure was 11-11-1 and the best training algorithm was the Levenberg-Marquardt. The relative error of over 90% data was less than 10% by the BP prediction model. The precision of BP model was obviously better than that of SRA model, and the former can more accurately reflect the inherent law between K and factors.