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In the sewing industry, in order to increase the degree of automation of the sewing process, manipulators are usually used to realize the loading and unloading of sewing materials. However, for sewing materials with random postures and positions on the assembly line, the traditional methods of teaching and pre-programming by industrial robots can no longer meet the technological requirements. Based on the analysis of the traditional SIFT (Scale-invariant feature transform) algorithm, this paper designs a sewing material visual positioning system based on the Harris-SIFT algorithm. Firstly, the system performs camera calibration on the vision positioning platform, establishes a geometric model of the image and eliminates distortion; secondly, it preprocesses the collected images and uses the Harris-SIFT algorithm to calculate the posture information; finally, it uses the positioning platform to experimentally verify sewing patterns of different shapes. Experimental results show that the system can quickly extract the characteristic points of the sewing material and achieve high precision matching. Its positioning accuracy reaches 0.2mm, the angle deviation is less than 0.15°, and the calculation speed is 4-5 times higher than that of the traditional SIFT positioning system, which can meet the technique requirements that the operating process of the sewing materials demand.