Abstract: Due to the inability of traditional workshop equipment operating status prediction to effectively utilize historical data for learning, limited real-time response ability, and difficulty in achieving good results in complex scheduling environments, a workshop equipment operating status prediction model combining digital twin and k-nearest neighbor algorithm is proposed. A digital twin model of workshop equipment entities in the information space is constructed, and then, a mapping relationship between equipment entities and models is also established, in order to obtain real-time feature data, namely equipment operating status feature data. The k-nearest neighbor algorithm is used to calculate the euclidean distance between the real-time feature data and the historical data, that is, to calculate the similarity between the current operating status of the equipment and the known historical status, and finally, based on the historical operating status data of the equipment corresponding to the first k distances, the current operating status of the equipment is predicted. The essence of this model is to collect real-time data from digital twin, obtain characteristic data of designated equipment operating status, and use k-nearest neighbor algorithm to predict the real-time operating status of equipment. Compared to previous studies, the contribution of this study is to improve the accuracy of equipment real-time operating status prediction.If digital twin and k-nearest neighbor algorithm are combined with relevant algorithms with self-learning ability, the predictive performance of the model will be better.