Multi-source data fusion is a powerful method to combine data from multiple sources to improve the potential values and interpretation performances of the source data. Multi-payload collaborative analysis is regularly used to detect the same target in planetary exploration. Therefore, it is of great significance and potential application to use spectral fusion to establish a more accurate and robust clustering analysis model for Martian minerals identification. In this paper, the spectral characteristics of the main Martian-related minerals were analyzed by using both visible near-infrared (Vis-NIR) reflectance spectroscopy and Raman spectroscopy. And some data pre-processing methods such as baseline correction, Savitzky-Golay smoothing, standard normal variate (SNV) scaling were used to produce a high-quality representation of the spectral data. Firstly, the information-rich spectral bands with higher signal-to-noise ratio and less overlapping were selected (i. e., Vis-NIR: 430～2 430 nm; Raman: 130～1 100 cm-1) for the clustering analysis. Secondly, soft independent method of class analogy (SIMCA) and principal component analysis-K-nearest neighbor (PCA-KNN), were respectively built based on selected Vis-NIR, Raman and two kinds of their fusion data(i. e., coaddition fusion and concatenation fusion), respectively. The accuracy of SIMCA model was enhanced from 72.6% (Vis-NIR) and 90.7% (Raman) to 96.3% (coaddition fusion) and 98. 1% (concatenation fusion). The accuracy of PCA-KNN model was improved from 68.9% (Vis-NIR) and 72.9% (Raman) to 80.3% (coaddition fusion) and 92.6% (concatenation fusion), respectively. The results indicate that the fused Raman/Vis-NIR data can improve the classification model’s accuracy of Martian-related minerals which will lay the foundation of quick rock classification for future Mars exploration.