系统发生网络可以描述物种在进化过程中发生的网状进化事件，也可以表示多棵系统树所隐含的冲突进化信息。鉴于目前系统发生网络构建算法的一些缺点，如时间复杂度高及所构建的网络依赖于输入数据顺序等，本项目研究基于有根系统树的系统发生网络构建算法。本项目首先改进系统树构建算法，通过扩展邻接法理论基础的方式改进邻接法，并且将改进的邻接法与RapidNJ 的搜索策略及 ERapidNJ 的外存方式相结合提出新的系统树构建方法，使得此方法在处理大数据集时更加快速有效。其次研究系统发生网络空间上度量的定义。在系统发生网络的构建过程中，需要计算系统发生网络间的距离，例如计算得到的网络与仿真网络或者真实网络之间的距离。本项目定义新的度量，使得此度量可以衡量更多网络之间的差异。最后提出系统发生网络构建新算法——基于簇集的不相容性及种子增长算法，使得此算法时间复杂度更低，同时所构建的网络更加准确有效。

Phylogenetic networks are able to describe reticulate evolutionary events that species occurred during evolution, and are able to represent conflicting evolutionary information implied by phylogenetic trees. In view of the current status of phylogenetic network construction algorithms, such as high time complexity and the constructed network dependent on the order of inpt data, this project study the phylogenetic network construction algorithms based on rooted phylogenetic tree. First the project studies the phylogenetic tree construction algorithms. An improved Neighbor-Joining (NJ) algorithm is proposed by means of extending the theory of NJ algorithm, by combining the improved NJ algorithm with the search strategy of RapidNJ and the external storage style of ERapidNJ, a novel method for constructing phylogenetic trees is created so that the method is more quickly and efficiently, especially for large data. Then the project researches the definition of metrics on the space of phylogenetic networks. In the process of constructing phylogenetic networks, one needs to compute the distance between phylogenetic networks, such as the distance between the constructed networks and the simulation networks or the real networks. The project defines a new metric that can measure the difference between the more network than other metrics. Finally the projest focuses on the phylogenetic network construction algorithms, designs a new algorithm based on the clouster incompatibility and seed-growing algorithm, the new algorithm has lower time complexity and the constructed network is more accurate and efficient.