According to the low accuracy and low stability of the single feature-based method for tungsten ore primary selection, a multi-feature fusion based on fuzzy support vector machine and D-S evidence theory was proposed. Firstly, the three types of vision feature that is color, gray and texture were extracted from the ore image after a series of image processing. Their probability function were acquired according to each type of feature utilizing fuzzy support vector machine and the results were used to D-S evidence theory as evidence. Finally, using D-S combination rule of evidence to achieve the decision fusion and giving final recognition result by classification rules. The experimental results show that the accuracy of multi-feature fusion methods is over 96% and it has good performance on accuracy and stability compared to the single feature-based method in tungsten ore primary selection. The accuracy and stability can meet the requirement of production process.