To explore the river network distribution of distributive fluvial systems, and to provide knowledge base information for creating reservoir modeling training images. Methods: Using modern geographic information software tools such as Google Earth, Global Mapper, and 91 Satellite Map Assistant, the surface river channel bifurcation characteristics of the modern Golmud Fluvial Fan were systematically measured. The river network pattern model of the fluvial fan was established using Horton's law and fractal methods.Results:The results show that: 1) The Golmud River Fan has identified 26 river channels from the apex to the lake inlet section, 882 nodes and 2162 river sections. The river channels along the spring line and below show different river network patterns; 2) There are a total of 64 nodes developed in the upper part of the spring line, with an increase of RB=1.8900 times the number of rivers in each level compared to the previous level, and an average length of RL=1.0095 times that of the next level, indicating that the characteristic parameters of river network morphology are RB=1.8900 and RL=1.0095; There are a total of 818 nodes developed in the lower part of the spring line, with an increase of RB=1.0279 times the number of rivers in each level compared to the previous level. The average length of each level of river is RL=0.9899 times that of the next level of river, which means that the characteristic parameters of river network morphology are RB=1.0279 and RL=0.9899; 3) The characteristic parameters of river network morphology vary greatly due to the influence of slope. If the slope is larger, the characteristic parameters of river network morphology are larger, and if the slope is smaller, the characteristic parameters of river network morphology are smaller. Conclusions:The quantitative characterization of river network pattern characteristic parameters has promoted the development of sedimentology in distributive fluvial systems, providing new knowledge base information for conducting reservoir modeling and sand body prediction in continental basins.