Crosslinked polyethylene cable is an important equipment in 10 kV distribution system， and its safety is very important. Making scientific judgments on cable repair decisions can help improve the safety and reduce the economic cost. In view of this， a cable risk assessment and repair decision method based on K-means clustering and random forest（RF） classification model is proposed. The method first defines the risk level and risk degree of the cable based on the insulation status of the cable. Then the K-means clustering algorithm is used to cluster the multi-aging index dataset and classify the risk level intervals to build a multi-aging index risk matrix. Based on the risk matrix of the multi-aging index， the classification labels corresponding to the multi-aging index are determined by using the comprehensive weight method. Finally， the classification model of the repair ways of the cables is established and trained based on the RF algorithm， and the selection results of the repair ways are output. The average accuracy of the proposed method reaches 99.70%， achieving rapid and reliable repair decisions for cables.