African Swine Fever (ASF) has been one type of acute, febrile, and highly contagious infectious disease during pig breeding. This infectious disease can be characterized by respiratory impairment and severe bleeding of the skin and internal organs. Four levels can be classified for the severity of the ASF disease, including the most acute, acute, subacute, and chronic. Once the infection by the strong strains can cause up to 100% morbidity and mortality in pigs. As such, the ASF has been mandated as the reporting animal disease by the World Organization for Animal Health (OIE), where China has been classified as a risk area for Class I animal disease. Given that there is no effective vaccine or treatment available so far, the prevention and control of ASF rely mainly on some protective measures, such as strict monitoring and health management. Therefore, it is a high demand for an effective risk assessment model to deal with the spread of ASF. This study aims to determine the risk areas of ASF in China, particularly for the decision-making on the risk assessment of the epidemic. A Bayesian Additive Regression Tree (BART) model was also constructed to assess the risk areas and influencing factors using the ASF outbreak data and 33 environmental factors from August 2018 to August 2021. Some buffers were set at every 5 km for the collected points of the ASF outbreak, in order to eliminate the possible bias from the clustering during modeling. One distribution point of two intersecting buffers was removed using overlay analysis, where there was only one ASF outbreak point per 5 km × 5 km grid. A Variance Inflation Factor (VIF) analysis was performed to initially eliminate the highly correlated variables. The high correlation among environmental factors was avoided for the high accuracy of prediction. Specifically, the factors with the VIF values greater than 10 were considered as the multi-collinearity for the removal in the VIF analysis. Subsequently, the environmental variables were verified, according to the importance level of the factors in the BART model. Finally, five optimal environmental variables were identified to establish the BART model for the assessment of the ASF risk areas, including the distribution of normalized urban land index, the normalized differential vegetation index, the mean annual temperature, the distribution of domestic pigs, and the coldest season precipitation. The results showed that: 1) There was the highest contribution of the normalized urban land index distribution (0.213±0.026), followed by the normalized differential vegetation index (0.207±0.028), the mean annual temperature (0.199±0.025), the distribution of domestic pigs (0.194±0.025), and the coldest season precipitation (0.187±0.026). 2) There was a relatively higher ASF risk in the small cities, compared with the other types of cities. The ASF risk also increased with the average annual temperature, the normalized difference vegetation index, and the number of pigs. The high precipitation during the coldest season reduced the likelihood of ASF. 3) The risk areas of ASF were concentrated in eastern and southwestern China, including the parts of three northeastern provinces (Heilongjiang, Liaoning, and Jilin), Hebei, Shandong, Henan, Shanxi, Shaanxi, Anhui, Jiangsu, Hubei, Hunan, Guangxi, Guangdong, Hainan, Guizhou, Sichuan, Yunnan, and Chongqing. 4) In the risk map, there was a higher uncertainty in southeastern China. Therefore, much attention can be paid to this area in the future, due to various influencing factors for the spread and occurrence of ASF. Additionally, the Area Under the Curve (AUC) was 0.90 in the accuracy assessment of the model, indicating the high predictive accuracy of the risk map. The findings can provide a strong reference to determine the risk areas and influencing factors for the occurrence of ASF in China, thus formulating the prevention and control plans.