Abstract：Rational construction of a well-facilitated farmland become necessary for food security and modern agriculture in China. In farmland construction, the automatic management of spatial data can benefit to accurately distinguish wellfacilitated farmland from the various land types. Various discriminating ways of well-facilitated farmland are adopted due to the different influence factors of agricultural productionin in different regions, where the spatial distribution and structural features of well-facilitated farmland are quite different from place to place across the country. Most of the previous studies focus mainly on policies, benefit evaluation, preliminary planning and design in the well-facilitated farmland construction. There is still lacking on the studies of the discrimination and analysis for the regional structural features of the well-facilitated farmland. In this paper, a framework of an index system was constructed to identify the spatial distribution and structural features of the well-facilitated farmland using the law of spatial differentiation together with the related national standards and strategic planning, ranging from the scale and shape of farmlands, irrigation and drainage facilities, traffic accessibility, and farmland protection ratio. The identifying system was designed on the basis of the index framework with C#.NET and Arc Object. Subsequently, the proposed system was verified for its availability by taking a case study of a LoessPlateau and a southern mountainous region. The results were as follows: (1) In the Loess Plateau area, the standard rate of the farmland scale, farmland shape, irrigation and drainage facilities, traffic accessibility was 87.01%, 83.12%, 80.52%, 90.91% and 83.12%, respectively. The well-facilitated farmlandarea can be identified by the system was 38.33 hm2 with the total discriminating precision was 94.38%. There were some farmlands that did not meet the requirements of the well-facilitated farmland, such as incomplete irrigation and drainage facilities, the irregular shapes of the fields that distributed in the branch furrow and furrow head around the farmland. (2) In the southern mountainous area, the standard rate of all spatial features was 100%. The well-facilitated farmland area that identified by the system was 30.07 hm2, which was 10.34 hm2 higher than that of the state-approved well-facilitated farmland. The high identified areas can be because some fields that meet the requirements of well-facilitated farmland have not been classed as the permanent basic farmland in some administrative regions. When regardless of this classification, the total discrimination precision was 100%. This index system can be used in different scenarios, such as macro monitoring and evaluation, project design and inspection of the well-facilitated farmland. This system was also applied forthe Northeast Plain, North China Plain, Loess Plateau and southern mountainous regions, while the key elements of the spatial features of well-facilitated farmland construction can be detected with a reliable discriminant resolution. When combining with the remote sensing technology, the index system can help automatic management to know the real-timespatial dataof well-facilitated farmland construction. Therefore, the developed system can be successfully applied to identify the extract well-facilitated farmland in the construction area, and then to analyzethe obtained spatial datain real time, finally to implement automatic management for well-facilitated farmland construction. Furthermore, the proposed system in this paper can offer a promising reference to the current comprehensive supervision platform, and improve the management effectiveness of well-facilitated farmland construction in modern agriculture.