Abstract: There are the prerequisites for effective separation of coal and gangue on strong environmental adaptability and high identification accuracy. Dual-energy X-ray is used to see through coal and gangue and image, which avoids the influence of external factors such as dust, light intensity and material surface. However, the radiation energy data collected by dual-energy X-ray detectors have defects such as afterglow effect, thickness effect and beam hardening effect. In order to reduce the influence of defects and improve the recognition rate of coal and gangue, a multi-dimensional analysis method of coal and gangue is proposed by combining R-value images with high and low energy image features. Firstly, high-energy and low-energy images are obtained based on dual-energy X-ray acquisition system, and R-value images are obtained by ratio method. Then, according to the three images obtained, the relationship between key physical parameters such as coal and gangue density and ash content and image features is studied, and a feature extraction scheme is designed accordingly. A total of 8 feature parameters are extracted to form a strong feature combination. Finally, relief algorithm is used to measure the importance of each feature parameter, and then classification experiments are designed. Taking rich coal, charred coal, gas coal and gangue from different areas as experimental objects, the accuracy of classification model was observed after removing the features with lower weight. It was found that PSO-SVM classification model had the best recognition effect on the mixed gangue of three kinds of coal with a recognition rate of 99.4% by taking the feature combination Rc, μlc, μl, R as input. Combined with the PSO-SVM classification model and the characteristic combination of Rc, μlc, μl, R, the identification and verification of mixed gangue of rich coal, charred coal and gas coal are carried out respectively. The results show that the identification rate of mixed gangue of rich coal is 98.89%, that of mixed gangue of charred coal is 100%, and that of mixed gangue of gas coal is 99.44%. Combined multiple features of multiple images for multi-dimensional analysis, the method finds that R-value image features and high-energy image features have the best discrimination degree to coal and gangue. What’s more, it can effectively reduce the influence of dual-energy X-ray defects, and realize higher recognition rate to different coal types with fewer features, which is superior to the existing methods. In addition, the ash content and density are taken as the reference to select the characteristic threshold, which meets the actual demand, reduces the frequency of parameter adjustment according to the coal quality difference in the mining area in engineering application, and improves the generalization ability of the identification model.