自2011年爆发以来，叙利亚内战已成为21世纪最复杂、持久的地缘政治冲突之一。这场内战不仅仅是叙利亚国内武装冲突的简单展现，它还融合了国际力量的博弈、宗教极端主义的兴起以及地区安全动态的重新配置。此前的研究主要从叙利亚国内的民族和宗教矛盾、美俄及周边国家在叙利亚的角力等方面入手，定性分析叙利亚内战的起因发展及其影响因素。定量研究方面，此前大多数研究以总体数据为基础，使用单一武装冲突数据库的数据对叙利亚内战的时空特征进行描述。
本研究基于复杂性科学方法，使用包括ACLED在内的多个武装冲突数据库进行量化分析，以准确把握叙利亚内战过程中武装冲突事件的时空特征。首先，结合叙利亚内战的死亡数据和地理坐标，绘制不同类型武装冲突的空间分布图，发现死亡人数主要集中在北部各族群交汇处和西南部首都大马士革附近。进一步使用莫兰指数验证不同类型武装冲突的空间自相关性，发现无人机空袭和导弹爆破、传统战争和对平民的暴力之间存在较强的空间自相关性。
其次，基于ACLED数据库，研究发现叙利亚内战中武装冲突总体数据的死亡情况呈现对数正态分布规律，细化到不同类型的武装冲突，除传统战争外仍符合幂律（大森定律）外，其他类型的武装冲突也符合对数正态分布规律。此外，本研究将对叙利亚内战的分析模式扩展到近年来发生的巴基斯坦、也门和缅甸的国内武装冲突，对这些国家的武装冲突进行分类型研究，最后比较和汇总。结果显示，不同类型的武装冲突死亡模式呈现不同规律，传统战争仍主要遵循幂律（大森定律），而新型战争中远程打击类型的武装冲突更多呈现对数正态分布。最后，从现代战争形态的变化和武装冲突数据库的统计方式改进两方面解释了这一新趋势的产生。
最后，通过叙利亚每日死亡人数的时间序列图和相应的重大事件，将叙利亚内战分为三个阶段。进一步研究叙利亚武装冲突事件的长程相关性，发现叙利亚整体武装冲突数据具有较好的长程相关性，表明叙利亚内战的死亡人数可能将继续保持缓慢下降的趋势。此外，还以叙利亚内战第三阶段为例，运用了长程相关性原理并结合具体事件去解释相应变化。

Since its outbreak in 2011, the Syrian civil war has become one of the most complex and protracted geopolitical conflicts of the 21st century. This civil war is not just a simple display of Syria's internal armed conflict; it also integrates international power plays, the rise of religious extremism, and the reconfiguration of regional security dynamics. Previous studies have mainly analyzed the causes and development of the Syrian civil war and its influencing factors qualitatively, starting from the ethnic and religious contradictions within Syria, and the tug-of-war between the U.S., Russia, and neighboring countries in Syria. In terms of quantitative research, most of the previous studies were based on aggregate data and used data from a single armed conflict database to characterize the spatial and temporal features of the Syrian civil war.
Based on the complexity science approach, this study uses multiple armed conflict databases, including ACLED, to conduct quantitative analysis in order to accurately capture the spatio-temporal characterization of armed conflict events in the course of the Syrian civil war. First, the spatial distribution of different types of armed conflicts is mapped by combining the death data and geographic coordinates of the Syrian civil war, and it is found that the deaths are mainly concentrated in the intersection of ethnic groups in the north and near the capital Damascus in the southwest. The Moran index is further used to verify the spatial autocorrelation of different types of armed conflicts, and it is found that there is a strong spatial autocorrelation between drone airstrikes and missile blasts, traditional warfare, and violence against civilians.
Secondly, based on the ACLED database, the study finds that the deaths in the overall data of armed conflicts in the Syrian civil war show a lognormal distribution law, which is refined to different types of armed conflicts, except for traditional wars, which still conform to the power law (Omori's law). In addition, this study extends the pattern of analysis of the Syrian civil war to the internal armed conflicts in Pakistan, Yemen and Myanmar that have occurred in recent years, and the armed conflicts in these countries are studied by type, and finally compared and summarized. The results show that the death patterns of different types of armed conflicts show different patterns, with traditional wars still mainly following a power law (Omori's law), while armed conflicts of the long-range strike type in new types of wars show more of a lognormal distribution. Finally, the emergence of this new trend is explained in terms of both the changes in the pattern of modern warfare and the improvements in the statistical approach of the armed conflict database.
Finally, the Syrian civil war is categorized into three phases by means of a time series plot of daily deaths in Syria and the corresponding major events. The long-range correlation of armed conflict events in Syria is further examined and it is found that the overall armed conflict data in Syria has a good long-range correlation, indicating that the death toll of the Syrian civil war is likely to continue its slow downward trend. In addition, the third phase of the Syrian civil war is taken as an example, and the principle of long-range correlation is applied and combined with specific events to explain the corresponding changes.