Malware phylogeny refers to inferring the evolutionary relationships among instances of a family. It plays an important role in malware forensics. Previous works mainly focused on tree-based model. However, trees cannot represent reticulate events, such as inheriting code fragments from different parents, which are common in variants generation. Therefore, phylogenetic networks as a more accurate and general model have been put forward. In this paper, we propose a novel malware phylogenetic network construction method based on splits graph, taking advantage of the one-to-one correspondence between reticulate events and netted components in splits graph. We evaluate our algorithm on three malware families and two benign families whose ground truth are known and compare with competing algorithms. Experiments demonstrate that our method achieves a higher mean accuracy of 64.8%.