It is of great practical significance to rapidly analyze the content of potassium (K) and sodium (Na) in wheat straw for improving its combustion efficiency. And totally 29 representative wheat straw samples collected from North China were chosen as the research objects. Based on the standard values measured by inductively coupled plasma mass spectrometry (ICP-MS), laser induced breakdown spectroscopy (LIBS) was used for the quantitative analysis of K and Na contents in wheat straw. In order to improve the accuracy of quantitative analysis, the spectral bands around the analytical lines of K and Na were primarily confirmed as original spectral data of the calibration models, respectively. The effects of baseline correction (BC), normalization (Norm) and meancentering (MC) on LIBS spectral denoising were compared. Moreover, the applicability of partial least squares regression (PLSR) and Adaboost backpropagation artificial neural network (BP-ADaboost) for preprocessed spectral data was compared and analyzed. Results showed that when compared with PLSR models, the BP-ADaboost models of potassium and sodium in wheat straw both had better effects, yielding R2p of 0.908 and 0.979, root mean square error of prediction set of 2.388g/kg and 0.138g/kg, relative percent deviation of 2.358 and 4.203, respectively. Therefore, LIBS technique can be used for the simultaneous quantitative analysis of K and Na in wheat straw.