In view of the different influence of different steady-state features on the identification results, and considering the misjudgment of minority classes caused by unbalanced data sets, a non-invasive load identification method based on feature weighted KNN is proposed in this paper. Firstly, the feature weight is calculated by entropy weight method, and it is used to improved feature distance calculation. Secondly, the voting weight is calculated according to the number of samples and the k value of algorithm, which is brought into the voting process to increase the classification accuracy of minority classes. The experimental results show that the average recognition accuracy of algorithm in this paper is 93.4%, which is 2.8% higher than that of KNN algorithm; For public data sets, the average accuracy and F1 score of algorithm in this paper are 86.8% and 81.6%, which are better than the other four classification algorithms.