Mineral prospectivity mapping (MPM) is a multi-factorial modeling process, which requires score assignments for individual evidential layers to use as their weights. The procedure of score assignment involves the measurement of weight values that reflect the favorable degree between each evidential layer and ore deposits. To achieve good mineral prospectivity results, an appropriate score needs to be assigned to each layer. The Expert estimation method, which assigns scores to evidential layers with the guidance of expert opinions, has been widely used. However, this kind of method requires expert knowledge and inevitably involves cumbersome trial-and-error steps. Moreover, the method will introduce bias into the prediction results. Proper score assignment is crucial for achieving reasonable prediction results, this study proposes a novel score assigning method, namely, the logarithmic mineralizing opportunity index (LMOI), to determine reasonable scores for each layer class in MPM. The LMOI makes use of a priori knowledge such as known mineral deposits in score determination. Additionally, it utilizes the distribution density information of the known deposits and the area ratio information of a layer class to enhance the correlation between the layer class and mineral deposits. To evaluate the effectiveness of the LMOI, a comparison experiment of MPM using a support vector machine (SVM) model was performed. Both the LMOI and Expert estimation method were applied to gold MPM in the Zhuji-Shaoxing area, Zhejiang Province, China. The aim of our experiment was to prove that different layer scoring methods have different effects on prediction results. The results demonstrated that the proposed LMOI can contribute to MPM and is easy to implement.