Rasterization is a conversion process accompanied with information loss, which includes the loss of features’ shape, structure, position, attribute and so on. Two chief factors that affect estimating attribute accuracy loss in rasterization are grid cell size and evaluating method. That is, attribute accuracy loss in rasterization has a close relationship with grid cell size; besides, it is also influenced by evaluating methods. Therefore, it is significant to analyze these two influencing factors comprehensively. Taking land cover data of Sichuan at the scale of 1:250,000 in 2005 as a case, in view of data volume and its processing time of the study region, this study selects 16 spatial scales from 600 m to 30 km, uses rasterizing method based on the Rule of Maximum Area (RMA) in ArcGIS and two evaluating methods of attribute accuracy loss, which are Normal Analysis Method (NAM) and a new Method Based on Grid Cell (MBGC), respectively, and analyzes the scale effect of attribute (it is area here) accuracy loss at 16 different scales by these two evaluating methods comparatively. The results show that: (1) At the same scale, average area accuracy loss of the entire study region evaluated by MBGC is significantly larger than the one estimated using NAM. Moreover, this discrepancy between the two is obvious in the range of 1 km to 10 km. When the grid cell is larger than 10 km, average area accuracy losses calculated by the two evaluating methods are stable, even tended to parallel. (2) MBGC can not only estimate RMA rasterization attribute accuracy loss accurately, but can express the spatial distribution of the loss objectively. (3) The suitable scale domain for RMA rasterization of land cover data of Sichuan at the scale of 1:250,000 in 2005 is better equal to or less than 800 m, in which the data volume is favorable and the processing time is not too long, as well as the area accuracy loss is less than 2.5%.