A distillation curve is an essential property for petroleum. Its features are beneficial for the modeling and optimization of oil-refining processes. To capture these features with a small number of parameters, an asymmetric probability distribution function-based distillation curve reconstruction and feature extraction method is proposed for the industrial oil-refining process. In our research, the expressive power of several frequently used probability distribution functions are first tested with some available distillation data. According to the statistics, the Kumaraswamy distribution function, one of the asymmetric probability distribution functions with four parameters, is identified as the best. Because not all distillation data are directly obtainable in the industry, the total probability theory-based data synthesis technique is adopted to estimate the key distillation points of unsampled streams, especially for the unmeasurable intermediate products at the outlet of a reaction system. Along with the distillation curve reconstruction, features of the synthetic distillation data are extracted by optimizing the parameters of the Kumaraswamy distribution function using the state transition algorithm. Industrial experiments were carried out to demonstrate the effectiveness of our proposal.