Microscopic urine analysis can help humans detect many problems and diseases in the body. Traditional microscopic urine analysis is time consuming and subjective, so computer-assisted automated microscopy can help overcome these problems. Urine crystallization can be used as an important criterion for the diagnosis of human kidney stones. This paper introduces an integrated method for the detection and identification of crystalline components in urine microscopic particles, in which crystalline particles, red blood cells, white blood cells, epithelial cells, and tubular cell samples are used in this experiment. Firstly, the canny operator is used to detect and locate the particles, and then use the MRF segmentation to extract the strong texture structure of the target object, so as to facilitate the extraction of subsequent recognition features. A new shape feature is proposed, the Sector statistical Fourier descriptor, combined with the corner feature and the circularity feature to form the feature vector for the SVM classifier. Compared with the Similar work, this paper simplifies the calculation with fewer features, improves the recognition speed, and the recognition accuracy is as high as 97.8%, and the recognition effect is stable.