近年来，脉冲星作为天文领域的研究热点，其研究资料对于电子通信、宇宙介质探测和计时等都有巨大的意义和重要性。因而，科学工作者们普遍期望能够知道脉冲星特征最符合的概率分布，以便用于相关研究。然而，虽然现在我们已经有了现成的方法来达到这些目的，但这些方法均存在一个难以避免的弊端：要么没能完全利用概率分布特性，要么过于浪费计算资源。为了能够既精确又简单地解决该问题，我们提出了一种融合了贝叶斯先验知识的综合拟合模型。该模型最大的优势是，可以帮助科学工作者自动用最优解来拟合脉冲星数据。 统计分布拟合以及回归拟合均为模型化数据的经典方法。两者之间既有细微的连接点，也有不同之处。正因为如此，在不同的情况下，两者各有其优劣。而对于处理自然数据来说，本文所阐述的脉冲星数据分析模型，在优化数据拟合方法的性能方面提出了有益借鉴。本文还将进一步探讨对综合拟合模型的理解并加以讨论。

Pulsar is a hotspot of the current research of today’s astronomy. And it also contributes a lot to timing, gravitational wave detection and electronic communications. Our astronomers are willing to comprehend the distributions that samples which are collected from pulsars are most likely to be subject to. Although there are existing methods for astronomical researchers to do that, they are either not fully-using statistical properties or computing-resource-wasting. As an advantageous and precise solution to the problem, we propose a comprehensive fitting model with Bayesian prior knowledge to help those researchers fit pulsar data into the optimal expression automatically. Both Statistical distribution fitting and regression fitting are both classic methods to model data. There are slight connections and differences between them, as a result they outperform each other in different cases. A analysis model for processing natural data, say astronomical pulsar data in this paper, is proposed to improve data fitting method performance. Then the insight behind the comprehensive fitting model is given and discussed.