Under the background of low counting rate, the high-precision measurement of X-ray spectrum is affected by the statistical fluctuation of X-ray flow, which determines the theoretical limit of given detector energy resolution, while the influence of other factors can be reduced by appropriate noise filtering and electronic technology. Previous studies on energy resolution mostly use spectral deconvolution to post-process the energy spectrum, so as to reduce the full width at half maxima (FWHM) of the characteristic peak. These post-processing methods are based on modeling the obtained energy spectrum as two random variables, i. e. input energy spectrum and detector response function, which is often computationally expensive and inefficient. A multi-pulse local average (MPLA) algorithm is proposed to optimize the X-ray spectrum data processing platform, which is an online real-time spectrum acquisition method. This method averages the pulse amplitude value in the dynamic window. MPLA algorithm involves two variable parameters; one is the average window size r, the other is the average pulse amplitude number n. The implementation process of the algorithm includes the following four steps: step 1, read the first pulse amplitude and locate an average window, update the current average window amplitude and pulse number after reading successfully; step 2, read the next pulse amplitude, judge the number of pulses in the average window after each update, and continue the third step when it is less than the preset parameter n, otherwise. Then perform step 4; step 3, continue to read the next pulse amplitude; step 4, average the pulse amplitude in the corresponding average window, and the average is the channel address to be updated and counted, and then clear the pulse amplitude and pulse number in the average window. In the part of theoretical derivation, this paper studies the transformation of the original probability density function (PDF) in the application of MPLA process, deduces the analytic expression of the probability density function obtained after the application of MPLA and proves the following characteristics after the transformation of MPLA probability density: (1) symmetrical distribution, MPLA retains the mean value and symmetry. (2) For single peak symmetrical distribution, MPLA reduces variance and sharpens distribution peak. In the experiment, the iron ore sample is used as the measurement object, and the results processed by the MPLA algorithm are compared with the results obtained by the traditional spectral method. The results show that in the typical case of the spectrum peak with normal distribution PDF, even if only two pulse heights are averaged, the FWHM of the transformed peak is narrowed.