In order to improve the weighted average estimation method for the basis properties of iron ore powder, 15 kinds of commonly used sintered iron ore powder are selected for assimilative and liquid phase mobility tests and thermodynamic calculations are conducted; then, Levenberg-Marquardt and generalized global optimization algorithms are used to fit one-and two-dimensional mathematical models for the basis properties values and chemical composition of iron ore powder, respectively; finally, the model prediction results are validated by using a randomized blending scheme. The results show that, the minimum assimilation temperature-onset melting temperature and the liquid fluidity index-effective liquid phase have a good agreement; the prediction of the two-dimensional mathematical model has a better Pearson correlation coefficient, and the average error of the predicted value is also significantly lower than that of the one-dimensional mathematical model. Within a certain range of minerals, the two-dimensional mathematical model based on chemical composition can be considered effective for predicting the basic properties of mixed ore.