Stone is a natural material that has many uses. One of the uses of stone is to use it as a building stone. Building stones have a wide range of applications, the main use of which today is used as a facade and decorative stone. The stone that is selected as a building stone must have a number of special physical and mechanical properties. The inherent properties of the stone are very influential in its extraction and processing, which has attracted the attention of many researchers to conduct research in this field. Building stones include inner igneous, outer igneous, sedimentary, and metamorphic rocks. The difference in the properties of these stones makes them have different applications and the way they are extracted and processed is different from each other. Extraction in building stone mines is mainly open and various machines such as diamond wire cutting machines, mortar, circular saw, etc. are used. This is an important issue; Investigating the relationship between the intrinsic properties of rock, which is a natural material, and operational factors in order to improve performance and various activities in mines and factories. In mining and quarrying, economic factors and costs are very important. Operating factors that affect costs. By examining and measuring the operational factors and their relationship with the properties of stone, it is possible to predict costs and other operational factors, which can be a very effective matter in the economic study and forecasting of quarrying mines and factories. One of the operational factors affecting mining costs is special cutting energy. This study has been done by examining a series of physical and mineralogical properties of granite rocks and their relationship with specific shear energy in order to estimate this parameter. Different modeling has been done by researchers to investigate the relationship between specific shear energy and the physical and mechanical properties of rock. In this study, various modeling has been done to investigate this relationship. The feature that distinguishes this study from other previous studies is the properties of the hard rocks that have been studied. The data in this study were analyzed using data mining methods in WEKA software. The relationship between the mineralogical properties of the studied rocks and their specific shear energy was investigated by linear multiple regression methods, multilayer perceptron neural networks, support vector machine, and M5P decision tree, and models were developed that are very simple and easy to use. They can be used to estimate the specific shear energy in new rock samples. The results obtained from these methods have been statistically analyzed and confirmed.