为减少X射线脉冲星信号辨识所需的观测时间,提出一种基于轮廓光子分布统计量的辨识算法.用该方法分析了观测数据按不同周期累积所获轮廓的光子分布差异,按累积周期正确与否,对轮廓进行分类建模.基于两类轮廓模型,研究了光子分布统计量的性质差异,以此进行信号辨识.利用罗西X射线时变探测器数据进行了辨识实验,结果表明该算法有效地减少了辨识所需的观测时间,且不存在误检问题. In order to reduce the observation time required for X-ray pulsar signal identification, an identification algorithm based on the profile photon distribution statistics is proposed, which analyzes the photon distribution differences of the observed data accumulated by different periods, Or not to classify the contour.According to two kinds of contour models, the difference of the photon distribution statistic is studied, and the signal identification is carried out.Using Rossi X-ray time-varying detector data to identify the experiment, the results show that the The algorithm effectively reduces the observation time needed for identification, and does not have the problem of false detection.