The invention provides a power transmission and transformation suspicious data screening method and device based on a random forest model, and the method comprises the steps: S1, selecting data of multiple dimensions according to the category and periodicity rule of power transmission and transformation equipment, and building a data feature item; S2, distributing different weights for the data according to the sampling time, respectively marking known normal data and abnormal data as positive and negative samples, and dividing a data set into K parts; S3, training a random forest model by adopting a K-fold cross validation method, iteratively adjusting the number T of trees in the random forest by taking an average value of positive and negative sample accuracy as a target, and obtaininga value of T when an index is optimal; and S4, screening suspicious data by using the trained model. Power transmission and transformation equipment is used as an object to construct a suspicious datascreening object, a random forest model of an optimization training set is used for learning data rules from a large amount of historical sampling data, power transmission and transformation suspicious data identification and screening are achieved, the workload of manual screening is reduced, and the data quality of an electric power regulation and control system is improved.