The use of oxygenated components is an effective way to suppress soot formation and reduce particulate matter emission in diesel engines. Based on the group additivity method, we proposed a new statistical model with kernel ridge regression (KRR) to predict the sooting tendencies of oxygenated components and interpret the experimental results of fuel sooting tendency. Through a training process, the relationship between fuel molecular structure and sooting tendency was built. In the testing process, the predicted values of the yield sooting index were in good agreement with the measured values, indicating that the KRR model had good predictive performance. The KRR method can be used to compare the sooting tendencies of various components that are important in traditional or new fuels. For example, based on the predicted results of typical five-carbon oxygenated compounds, the inhibiting effects of oxygen functional groups on sooting tendency are in the following order: esters > ketones approximate to aldehydes > ethers > alcohols. In addition, the proposed method provides statistical evidence for the effects of structural features on sooting tendencies. The predictive results for typical ethers indicate that although sooting tendencies increase as carbon chains become more branched, they are not very sensitive to the specific arrangement of the branches compared with the effect of carbon chain length. Moreover, the KRR method is useful for studying non-nearest-neighbor interactions, including functional group interactions. By predicting the sooting tendencies of typical esters, the coupling of ester moiety and C=C double bonds shows an overall enhancing effect on sooting tendency, which indicates that C=C double bonds play a dominant role in soot formation. This method provides an alternative way to obtain the sooting tendencies of fuel compounds and verifies the selection of low-sooting fuels from a wide range of feedstocks.