Abstract: Objects:  The mountain peak extraction technology determines the accuracy of the hill position classification results and the efficiency of automatic classification of micro-landform. On account of the limitations of elevation and contour lines, such as loss of local mountain peaks and incomplete removal of false mountain peaks, it must be mined other landform factors to express the distribution feature around the mountain peaks.   Methods:  Based on the uniform distribution feature of the aspect around the mountain peaks, an efficient mountain peak extraction model is proposed in this paper, in which the aspect is centered on the mountain peak and gradually increases clockwise. Moreover, according to the ridge line fitting method and the recursive thought of depth first search algorithm, the false mountain peaks in the extraction results are removed, while the real mountain peaks are retained.   Results:  Considering the negative influences of the terrain fragmentation of the entity DEM, this paper experiments and analyzes with simulated DEM and entity DEM respectively. The result shows that the proposed method overcomes the uncertainty of subjective threshold when extracting mountain peaks based on closed contour lines. The accuracy of extracting simulated DEM mountain peaks can achieve up to 100% due to its continuity and smoothness. Considering that the terrain fragmentation of entity DEM will make it ill-posed, we improve it by adjusting the aspect distribution constraint condition and obtain average extraction accuracy of 96.1%.   Conclusion:  An efficient mountain peak extraction model is proposed in this paper based on the digital terrain analysis technology and the uniform distribution feature of the aspect around the mountain peaks, and the topographic feature points are extracted from the perspective of terrain geometry. Compared with the traditional mountain peak extraction methods, the method based on aspect distribution feature is relatively accurate and simple, effectively reducing the false mountain peaks appearing in the results.