Coal is the major energy resource in china and accounts for 74% of the primary energy constitution.Coal mining leads to land damage,water loss and soil erosion,and it is the main cause for the increasingly lack of land resource.Further,it also brings about severe environmental and social problems in mining areas and takes adverse effect on the production activities and the life of local farmers and social stability.So the land reclamation of mine area has been the major research point in China.However,the land reclamation begins later in China,and more attention is paid to the engineering work,the reclamation soil isnt study enough,which leads to the lower productivity of reclamation land and bad soil conditions.So systemic studying the change rule of the soil characteristic in reclamation has an important role for land reclamation.The GIS can be a useful tool for the research.In this paper,a digital reclamation soil characteristic model based on DTM is put forth in this paper.Then the method of soil productivity evaluation based on this digital model is introduced.For the sake of facility,the DTM that describes the reclamation soil property is defined as the digital reclamation soil model.The structure for digital reclamation soil model can be vector or grid.Through analyzing,the grid data structure is more convenience because its base unit will be fixed no matter what kind of operation is taken.In addition,grid structure is easy for logical and mathematics operation.The model can depict the analyzed result and soil character.through visualization which can reflect the distribution status of every soil character index objectively and accurately.The method of soil productivity evaluation based on this digital model is introduced; then the improving measures can be provided based on the information obtained form this digital model.In addition,the method of describing the distribution complexity of soil character with information entropy is introduced.The application of this model to land reclamation in mining area can improve the decision level in soil improvement.