Zinc smelting leaching slag is the solid smelting waste produced by the hydro-zinc smelting process, accounting for more than 75% of the total output of zinc smelting solid waste. Because it contains Zn, Cu, Pb, Ag, Cd, As and other valuable metals elements, it has great potential in resource utilization. However, due to its unstable composition content and insufficient detection accuracy, it is not easy to guarantee the resource conversion efficiency of key elements. Therefore, accurate quantitative analysis of the key resource components of the leaching residue is of great significance in the green development of zinc smelting. In this paper, five target elements of Zn, Cu, Pb, Cd, and As are the analysis objects,the method of XRF working curve and the method of XRF combined with RBF neural network model used to quantitatively analyze the target elements of the leaching residue. The relative error and Relative standard deviation are used as evaluation indicators of the two methods to compare the performance of the two methods. First, the concentration gradient samples of zinc leaching residue collected in the industrial field were prepared by standard addition method, used as standard sample and detected by ICP-OES. Then the detection result of ICP-OES is used as the reference value for the quantitative analysis of the target element, the concentration gradient sample is detected by X-ray fluorescence spectroscopy (XRF), to establish the working curve of target elements, the working curve is used to analyze each target element quantitatively. At the same time, the XRF spectrum data is used to construct the input matrix, the target element concentration of the sample is used to construct an output matrix, and the RBF neural network is trained to construct the multi-element calibration model of the target element in the leaching residue. This model is used to realize the target element prediction of the leaching residue sample. Compared with the ICP-OES reference value, the average relative error and standard deviation of the working curve method are 8.5% and 4.0%, respectively; Compared with the ICP-OES benchmark value, the average relative error and standard deviation of the RBF neural network are 0.18% and 0.58%, respectively. The results show that both methods can achieve the quantitative analysis of target elements of the leach residue samples, but XRF combined with RBF neural network can achieve the accurate quantitative analysis and matrix correction of the leach residue samples. The accuracy and precision of the analysis results are better than the traditional working curve analysis methods.