In view of the flash point prediction of constant line aviation kerosene, a soft sensor method based on the grey correlation analysis (GRA) and improved whale optimization algorithm (IWOA) is proposed to optimize the extreme learning machine (ELM). GRA is used to calculate the information correlation degree between each auxiliary variable and the variable to be tested. Auxiliary variables are selected as inputs through the experimental method, and then IWOA is used to find the optimal weight threshold for ELM. In the early stage of the algorithm iteration, the improved Tent chaotic mapping is used to initialize the population to make the population distribution more uniform. The adaptive weight is combined with a random difference variation strategy to improve the optimization ability of the algorithm. The effectiveness of the improved algorithm is verified by eight benchmark test functions, and the improved model is proven to be effective in predicting flash points by the actual flash point data of the constant line aviation kerosene in an atmospheric tower of a refinery.