Aromatic hydrocarbon yield is considered as one of the important product quality indicator in catalytic reforming production process. Aiming at the difficulty of the aromatic hydrocarbon yield online measurement, a soft sensor modeling method of aromatic hydrocarbon yield is proposed based on mutual informationimproved gravitational search algorithm (MIIGSA) optimized extreme learning machine (ELM). Firstly, the MI method is used to extract the most relevant process feature quantities and perform dimension reduction processing, and the auxiliary variables of the soft sensor model are determined. Secondly, through introducing the successive quadratic programming (SQP) method and chaos mutation strategy, the IGSA with good global optimization performance is constructed. The IGSA algorithm is then applied to optimize the hidden layer threshold parameters and input weight parameters of ELM, and the optimization target considers the minimization of both the root mean squared error (RMSE) of the model output and the number of conditions of the hidden layer output matrix. Finally, the aromatic hydrocarbon yield soft sensor model is established based on the IGSA optimized ELM method. The proposed model was applied in the prediction study of the aromatic hydrocarbon yield of the catalytic reforming equipment in a certain refinery plant, the simulation results show that the proposed soft sensor model possesses promising prediction accuracy and reliability.