A classification system based on a convolutional neural network was performed to recognize the different combustion patterns of Cu concentrate-SiO₂ mixtures tablets under oxidation gas to estimate their combustion behavior and phase changes in flash smelting. A suspended-combustion-test method involving high-speed digital microscopy and thermal measurements was employed to characterize the combustion behavior of each sample. The time series images-based pattern recognition method enabled the calculation of the chemical composition of the blended concentrates by transforming the network output into a probability distribution. The combustion of the blended-concentrate tablet was different from that of each single-concentrate tablet in terms of the combustion pattern, such as the shape of the molten part, and the temperature change pattern. It is interpreted that the change in the free surface shape of a tablet is an important region for combustion pattern recognition. Thus, only when blended samples were used as training data as well as single samples, a good correlation could be obtained between the measured and predicted values of its chemical compositions.