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It is a great significance to study the feature distribution law of track image to improve the matching rate of track image and rebuild the three-dimensional modeling of the track. Because the background of the track image is complex, the color information of the image is single, and the features distribution of track image is variable, it is especially important to study the distribution law of the feature points of the track image. In this paper, we analyze the track image firstly. Based on the geometric features and layout of the track components, we divide the image into several different blocks. Then, using the scale-invariant Harris feature point detection algorithm to extract image feature points in different blocks; describe the obtained feature point with Sift descriptors, and finally, match the feature points; Analyzing the geometric feature points distribution of track image with Statistical methods. We experiment 1000 track images which were taken from real word, detect the geometric feature point distribution and got the show up frequency conclusion as following: 97.8% of the baffle seat area (the apex of the baffle seat), 57.3% of the nut area (nut corner point), and the elastic bar area (the elastic bar inflection point) ) 53.9%, rail sleeper intersection area (rail sleeper intersection boundary point) 24.7%.