The rapid and accurate quantitative analysis of the total iron (TFe) content in iron ores is extremely important in global iron ore trade. Due to the matrix effect among iron ores from different origins, it is a major challenge to accurately determine the TFe content of iron ores by laser-induced breakdown spectroscopy (LIBS). The double back propagation artificial neural network (DBP-ANN) proposed in this paper provides a solution to improve the accuracy of LIBS in determining the TFe content of branded iron ores, which is a combination of pattern recognition and regression analysis based on BP-ANN. In this study, LIBS spectra of 80 batches of representative iron ore samples from 4 brands were collected. The univariate regression methods based on brand-independent and brand-hybrid were analyzed and compared for determining the TFe content of branded iron ores, and the multivariate model based on DBP-ANN was constructed for the first time. BP-ANN was employed to establish different quantitative models of the TFe content of each type of brand after brand classification of iron ores based on the BP-ANN algorithm. Compared with the brand-hybrid BP-ANN, the coefficient of determination (R²) of the test samples using DBP-ANN increased from 0.972 to 0.996, and the root mean square error of prediction (RMSEP) and the average relative error of prediction (AREP) were reduced from 0.456 wt% and 0.584% to 0.177 wt% and 0.228% respectively. Moreover, the prediction error based on the DBP-ANN model was within the error range (<0.275 wt%) accepted by the traditional chemical analysis method GB/T 6730.5-2009. Meanwhile, the established DBP-ANN method was also compared with the common multivariate method, and it showed better analytical performance. The results showed that LIBS combined with DBP-ANN has the potential to achieve rapid and accurate analysis of the TFe content of branded iron ores.