Abstract: The development morphology of coal rock cracks is an important factor affecting the permeability of coal rock and determining the mechanical features of coal rock mass. A coal rock crack recognition method based on connectivity threshold segmentation is proposed to address issues such as improper handling of complex structures, insufficient preservation of crack boundary features, and noise interference in the process of recognizing coal rock cracks. Firstly, histogram equalization enhancement algorithm and non local mean filtering denoising algorithm are used to preprocess the image. Secondly, adaptive Otsu threshold segmentation is used to determine the threshold of the preprocessed image, recognize possible crack areas, and apply morphological operations to refine these areas, further highlighting the boundary features of cracks. Thirdly, seed points are extracted by Canny edge computing to recognize key features in the image. Finally, based on these seed points, regional growth operations are performed to effectively suppress noise and highlight crack information more clearly while smoothing image cracks. The experimental results show the following points. ① The mean square error of connectivity threshold segmentation is reduced by an average of 7.20 and 7.10 dB compared to adaptive Otsu threshold segmentation and adaptive threshold segmentation, respectively. The peak signal-to-noise ratio of connectivity threshold segmentation is improved by an average of 0.60 and 0.59 dB compared to adaptive Otsu threshold segmentation and adaptive threshold segmentation, respectively. ② Connectivity threshold segmentation not only effectively solves the problems of unclear crack extraction, poor end extraction performance, and disappearance of connection features, but also significantly reduces the interference of noise, making crack features more prominent, thereby greatly improving the accuracy and completeness of crack recognition. ③ On the basis of adaptive Otsu threshold segmentation, connectivity threshold segmentation enhances crack features and effectively eliminates noise points. The average accuracy is improved by 8% and 0.8% respectively compared to adaptive threshold segmentation algorithm and adaptive Otsu threshold segmentation, reaching 98.9%.