Abstract: Abstract: Land use and coverage change (LUCC) is an important research field of global change. Currently, land use and coverage change research focuses on the analysis of land use structures during the same period, and structure changes in the overall quantity of land use between time-series periods. While, the study of quantitative analysis of land use evolution process in different periods is deficient, which results difficulty to profoundly reveal the temporal and spatial patterns and evolution laws that may exist in the process of land change. Therefore, in this paper, we proposed a spatiotemporal pattern analysis of land use change based on the spatiotemporal process of land use. In this paper, the spatiotemporal variation sequence data of land use was the study object. Firstly, the spatiotemporal variation sequence of land use intensity in the study area was extracted by the spatiotemporal network. Secondly, as the basic measurement tool, the cumulative value of land use intensity change (CVLUIC) of different spatiotemporal variation sequences of land use was calculated. Thirdly, the CVLUIC is divided into three grades by K-means clustering. Finally, the spatiotemporal clustering analysis of different land use change grades was carried out. From the global perspective, the spatiotemporal pattern of land use change process was measured by the nearest neighbor distance analysis method. On the other hand, based on Ripley's k function, the multi-scale impact of traffic network on land use temporal and spatial change patterns was analyzed. The results showed that the dramatic land use intensity change (Type I) and the gradual land use intensity change (Type III) over the 10-year period from 2008 to 2017 in the study area showed significant clustering characteristics. While, the spatiotemporal clustering characteristics of the more severe land use intensity change (Type II) between Type I and Type III was not significant, The results showed that the human disturbance activities have obvious spatiotemporal heterogeneity during the study period. Additionally, the traffic network had a strong influence on land use and coverage change. The three types of land use intensity were clustered in the range close to the traffic network (in the range of 300 m and 600 m), and tend to be randomly distributed in the farther distance (within 900 m), which showed that the transportation network had a strong impact on land use change. At different spatial scales from the transportation network, the dramatic land use intensity change (Type I) and the gradual land use intensity change (Type III) showed significant significance on the small spatial scale, while the more severe land use intensity change (Type II) at multiple scales was not significant. This indicated that there were scale differences in the impact of transportation network on spatial and temporal changes of land use degree, and more extreme land use change processes (Type I and Type III) were more likely to show spatial and temporal aggregation. Through analyzing the land use change from the perspective of the spatiotemporal evolution process, our work will be useful supplement to the LUCC research.