Abstract: In order to meet the requirement of rapid response of resource rescheduling in intelligent manufacturing shop and the characteristics of invisible disturbance difficult to measure and capture, a decision-making method of resource monitoring and rescheduling in intelligent manufacturing shop was proposed. Firstly, a resource abnormal state monitoring model is established based on the good continuous monitoring performance of support vector machine. Secondly, the accuracy of SVM model was improved by combining Lasso regression algorithm and K-nearest neighbor value classification algorithm, and the fault-tolerant mechanism was constructed by data substitution method to ensure the transient smooth operation of the system in case of anomalies. Then, the workshop resource rescheduling method is designed, and the classifier is trained for rescheduling scheme selection by historical case data to guide the efficient production of intelligent manufacturing workshop under the condition of invisible disturbance. Finally, the effectiveness of the proposed rescheduling decision method is verified by an example of actual workshop invisible disturbance.