Conventional DGA fault diagnosis methods of power transformer can only judge whether the equipment is in normal or fault condition, but they can neither characterize the potential failure of the normal transformer nor identify the trend of transformer converted into fault state. In order to solve this problem, this paper proposes a transformer state evaluation method based on clustering and time series analysis method. Firstly, the data preprocessing method is carried out to prevent the influence of noise. Then, based on big data clustering method, the proximity of sampled data and historical fault data clusters is calculated. Based on the result, the equipment condition is sorted into healthy, incipient faulty or faulty. For equipment in faulty state, the fault type is identified. For healthy equipment, the health score is calculated based on related weights of fault types. For incipient faulty equipment, the time series similarity analysis method is used to further predict the time span for the equipment changing from current state to fault state. The example analysis verifies the feasibility and effectiveness of the proposed method. This work is supported by Key Program of National Natural Science Foundation of China (No. 51537010) and Science and Technology Project of State Grid Corporation of China (No. 52182016001J).