Abstract: Scale effect is one of the core issues in the study of landscape ecology, which is the basis for correctly understanding and perceiving the interaction between landscape pattern and ecological process and its dynamic change characteristics. For the problem of strong scale dependence in the spatial pattern and connectivity analysis of the green infrastructure (GI), land-use classification of the Duliujian River Basin in Tianjin was extracted using ArcGIS 10.1, ENVI 5.3 and eCognition 8.9 software. Then the methods of morphological spatial pattern analysis (MSPA) and landscape connectivity analysis were used, and the GuidosToolbox 2.6 and Conefor 2.6 were used as software tools. Finally, the grain effect, edge effect and distance effect of GI in 2016 were analyzed quantitatively and evaluated by setting different grain size, edge width and diffusion distance. The results show that:(1) Morphological spatial pattern (MSP) types had obvious grain effect and edge effect, and smaller grain size and edge width would obtain more detailed spatial pattern information. (2) The grain effect changed the spatial distribution of GI pixel, which lead to the loss or increase of GI space information, and finally affected the spatial configuration and quantization relationship of MSP types directly. However, the edge effect did not change the spatial distribution of GI pixel, but the impact on MSP types was even more pronounced. (3) Landscape connectivity had a direct relationship with the diffusion distance. The greater the distance was set, the higher the landscape connectivity of the GI core area was. When the diffusion distance increased to a certain extent, all core areas would be connected into a network as a whole and the maximum connectivity of the landscape would be achieved. The pivotal diffusion distance of the GI core area in the study area was 2.5-4.5 km. The research result shows that the scale effect of GI elements in the Duliujian River Basin is obvious, but the mechanism is different. MSPA and landscape connectivity analysis method can reflect the spatial change characteristics and numerical change rules of various landscape types and network components more intuitively.