In this study, we processed the flame images of biodiesel combustion in industrial furnaces, classified and evaluated flame states using digital image processing techniques, and proposed a combustion stability index (CSI) using the particle swarm optimization (PSO) algorithm. In order to more accurately predict the combustion stability under different oxygen concentrations, we proposed a method that combines the Multi-Input Radial basis function neural network (RBF-NN) with empirical mode decomposition (EMD). Initially, the EMD method was employed to decompose the original time series of CSI. Subsequently, a decomposition model incorporating initial parameters and CSI was established using the radial basis function. The results of the computations indicate that the EMD-RBF-NN model significantly outperforms existing models in enhancing the accuracy of CSI.