Abstarct: The main purpose of this study is to introduce a geographic information system (GIS) baxsed, multi-criteria decision analysis methods for selection of favourable environments for volcanic-hosted massive sulphide (VHMS) deposits. Accuracy incretion and search space reduction are the main purposes in production of mineral prospectivity maps. Choice of the suitable method for information integration in the geographical information system which has the most consistent with the nature of the used data, is the first step in achieving to this goal. Due to the fact that exploratory data are not independent and most of them has feedback among criteria and sub-criteria and according to the one-way dependence condition of the evaluated data in the Analytic Hierarchy Process (AHP) approach, an Analytic Network Process (ANP) approach is assigned. Also, the technique for order of preference by similarity to ideal solution approach (TOPSIS) was used to transfer ANP results between zero and one and prioritize pixels in the geographic information system. In the following, the knowledge-guided ordered weighted averaging approach, which the weights are independent of the criterias, were used and the results were compared with other approaches results. In the end, the knowledge corrected Dempester Schafer aapproach, were used for uncertainty modelling and decreasing of suggested area. In this research, the exploratory data related to the Bavanat Region in Fars province are used, in case of providing the mineral prospectivity model for massive sulfide deposits. The used data include geological data, remote sensing, stream sediment data and geophysical data. In practice, it has been attempted to achieve the highest accuracy of results with the least amount of proposed search area compared to other conventional methods in order to achieve the lowest loss of mines and mine indicators in the results. AHP as well as ANP approach, is knowledge-baxsed approaches. Regarding to the filed and official validation of results, the ANP results is more reliable and has less suggested search area than AHP approach. baxsed on the results, suggested areas of ANP are 17% less than AHP (1138.4 Km2 in AHP has decreased to 952.6 Km2 in ANP), while, the accuracy of the results has increased more than 6%. The receiver operating characteristic (ROC) curve index (area under the curve) shows that AHP approach ROC is 0.7609, which was improved by the ANP approach to 0.8275. By using the AHP-OWA method, we decreased 9% of the search area, in comparison with the OWA approach. This approach increased also the accuracy of our results by 9%, which is more than the OWA approach. Our approach reduced the search area by 1% and increased of the accuracy of the results by 14%, in comparison to other knowledge-driven approaches such as the AHP approach. Application of new knowledge-guided OWA approach by changing linguistic variables according to the conceptual model of VHMS deposits made extraordinary results. This method predicted all discovered mines with 100% accuracy. The suggested area decreased to half part of the previous suggested area (851.6 Km2). According to the input data, by using Dempster- Shafer knoeledge-guided data driven approach, the uncertainty has modeled in each pixel of the map. The proposed area of this approach is 206 square kilometers, which greatly reduced the area of the proposed range. The classification accuracy of this approach is 89%, which is very reliable given the use of the pessimistic operator AND in the final integration stage. This amount achieved by using the OR operator with a precision of 95.46%. Regarding the results of this approach, if we plot the characteristic curve of the system's performance (ROC), the area under the curve will be equal to 0.7629, which, due to the test data is calculated (not the trainig data), it is a proper result.