Abstract: Abstract: Lameness diseases could cause high elimination rate of the worthless dairy cow, and early detection of dairy cow lameness disease is a significant research field of dairy farming. In this research, a lameness detection method of dairy cows with the fusion of LCCCT (local circulation center compensation tracking) and DSKNN (distilling data of k-nearest neighbor) was proposed. By using the normal background statistical model (DNBSM), the dairy cow videos were decomposed into image sequences, and segmented to target region and backgrounds. Then, the obtained pixel area of the upper contour of the cow body was extracted by the LCCCT. In the detected region, DSKNN were used to extract the slope data of the head, neck and the back connected to the neck region. Firstly, the DNBSM model is used to separate the target dairy cow's pixel area and background from the dairy cow's sequence image. Since the frontal movement of dairy cow body is greater than the back movement of the dairy cow body, the DNBSM algorithm is used to lead to a better detection of the front body pixels for the dairy cow. Then, the LCCCT model is used to track and extract the pixel area of the front dairy cow body, and the DSKNN model is used to extract the target's head and neck for slope data of contour line fitted. The changes in the slope data of the fitted straight line of the dairy cow's head and neck were used as the basis for the detection of lameness of dairy cows. The characteristics of the slope data of head-neck fit lines of different dairy cows are different. Compared with other body parts of dairy cows, the head and neck area data of dairy cows are relatively easy to obtain. When dairy cows stand or walk, the head and neck characteristics can be extracted stably, which is the basis for judging lameness. The upper contour of the dairy cow's body starts from the tip of the dairy cow's head and ends beyond the neck line. If the extracted contour line belongs to a defined area range, it is considered to be a valid contour line. Outside this range, it is considered that the extracted contour line is abnormal or has an error. The videos were divided into 3 slope data sets with 3 kinds of labels including slight lameness, moderate-severe lameness, and normal based on large sample video sequence frame data. In order to verify the validity of the algorithm, a total of 18 videos of dairy cows were processed. The slope data set was obtained from fitting line at the junction of the head, neck and back region. On the original data set, SVM, Naive Bayes algorithm and KNN classification algorithm were used to test the accuracy of dairy cow lameness detection, and the detection correct rates of SVM and Naive Bayes algorithm were both 82.78%, which were higher than that of KNN algorithm which was 81.67%. Test result illustrates that the slope feature of fitting line at the junction of the head, the neck and the back can be used to detect dairy cow lameness diseases. After cleaning the original data set, the correct detection rate of lameness classification by KNN classification algorithm was 93.89%, and the correct detection rates of SVM classification and Naive Bayes classification algorithm were 91.11%, and 86.11%, respectively. The results show that the KNN classification algorithm can get better results using cleaned data set. The results of this research can provide the reference for the prevention and diagnosis of lameness disease of dairy cows.