Abstract: ob＜x＞jective ba＜x＞sed on machine learning and threshold segmentation, the number and aggregation degree of Caenorhabditis elegans in images can be counted, and the efficiency and accuracy of social behavior experiments can be improved, to facilitate the use of small model organisms such as nematodes for high-throughput screening of potential neurobehavioral drugs。Methods About 5000 samples of medium containing nematodes were collected, and an algorithm of nematode identification and detection ba＜x＞sed on machine learning and threshold segmentation was designed。Results ba＜x＞sed on machine learning and threshold segmentation algorithm, a software was designed to automatically count the number and aggregation degree of nematodes. Compared with the results of artificial statistics, the analysis time was significantly lower than that of artificial counting, the detection time of each image is in the millisecond level, and the detection accuracy is better than 90% for single nematode 。Conclusion This method solves the problems of time-consuming and low efficiency of the total number and aggregation of Caenorhabditis elegans, which is a small and medium-sized model organism。