Abstract:  Granular computing (GrC) is a powerful tool to handle imprecise, uncertain and incomplete information. Granulation of problems, computing of granules and integration of the results to granulated problems constitute its basic ideas. Partial covering is a global model in GrC, which has the potential application values in the research fields such as computer security, search engine and customer evaluation. As a special case of the partial covering (the global GrC model), the full covering had been studied from the perspective of the rough sets, which is depicted under the framework of the general topology. This article developed the granulation, knowledge approximation and the properties of approximation operators of the full covering from the perspective of GrC under the framework of pre-topology theory. Firstly, the concepts of the neighborhood system, the granule and the full covering GrC model were presented, and the full covering approximation space was also defined, and then the neighborhood system of the full covering GrC model was regarded as a basic granule. Secondly, followed by the concepts of interior and closure operators in the pre-topology space, the interior and closure operators of the full covering GrC model were defined by those basic granules, which could be used to approximate any object in the full covering approximation space, and some examples were also illustrated. Finally, the fundamental properties of those two operators were explored under the full covering approximation space, such as monotone and duality, and the proofs were also demonstrated for the proposed theorems and corollaries. All the work was the foundation for axiomatic systems of the operators in the full covering approximation space, and the feature selection algorithm based on the full covering GrC model is the next research in the future.