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A particle size analysis of the ore size is required with a view to improving the production quality of the concrete industry. The traditional method is to use manual sieving processing, which requires a lot of labor and material resources. At the same time, there are also problems such as long detection time and low detection accuracy; To address this problem, a new approach to ore particle size classification detection based on improved watershed-concave segmentation is proposed by using computer vision technology. Initially, an adaptive median filter and improved multi-scale morphological processing are used to extract ore contour features. Secondly, the combination of improved watershed segmentation and concave point segmentation is used to obtain the set of deep concave points formed by adhesions between ores. Finally, an inverse chain code template is introduced to effectively separate the set of concave points to make an accurate statistical analysis of the ore grain size. According to the experimental results, the cumulative error rate between the particle size classification of this algorithm and the particle size classification of manual sieving is within 5%. Therefore, this algorithm has high accuracy and practicality, and is worthy of vigorous promotion and application.