In the power grid outage user sensitivity and complaint prediction, the inaccurate prediction results affect the precise service of the power grid company, so a situational awarenessbased grid outage user sensitivity and complaint prediction method is designed. Through the Enterprise Miner workstation module and the Enterprise Guide module in the SAS software, the grid power outage user sensitivity and complaint-related data are collected, including the power outage sensitive user tag data, fault handling data, power outage event data, customer call data, and 95598 work order data. Perform preprocessing on the mining data such as missing data processing, abnormal data processing, and alarm false positive and false negative data processing. Based on situational awareness technology and random forest algorithm, a grid outage user sensitivity and complaint prediction model is constructed to realize user sensitivity to outage and complaint prediction. The method is used to predict the sensitivity and complaints of users about power outages in a power grid in a certain area, and the prediction performance of the method is tested. The test results show that the method has a precision and recall rate higher than 90%, the F-measure data value is high, the AUC area is large, and the data sensitivity is always greater than 97%, indicating that the design method has superior grid outage user sensitivity and complaint prediction performance.