The analysis and prediction of the factors of a transmission line defect state can provide important technical support for transmission line patrolling. Based on the analysis of a transmission line state and the study of influencing factors, a method for the state prediction based on association rule mining is proposed. First, the transmission line defect state is evaluated based on historical defect data. Combined with various influencing factors, the characteristic database of the line defect state and related factors is constructed. Then a FP-Growth algorithm is introduced to mine the association rules between each factor and defect state, and the obtained rules are used to predict the defect state of the line. Finally, taking an overhead transmission line in a region as an example, defect state samples are obtained through historical defect data evaluation, and the relevant condition features are extracted as input features, and used to predict the defect state of the line. The results verify the effectiveness of the method presented, and this has a reference value for transmission line patrolling and maintenance. This work is supported by the National Natural Science Foundation of China (No. 61773126) and the Science and Technology Project of Guangdong Power Grid Co., Ltd. (No. 031800KK52180074).