An intelligent fault diagnosis method used for magnanimous monitor data is proposed to deal with the problems of power transformer fault diagnosis. Firstly, a selfpowered RFID sensor tag is designed to measure the transformer vibration signals, which has advantages of simple structure, convenience and low cost. The measured transformer vibration signals have characters of large quantity, high dimension, complex components and low signal to noise ratio. A stacked autoencoder (SAE) of deep learning is employed to extract features of the measured vibration signals, where features of the same status are highly aggregated and features of the different statuses are obviously separated. A weighted native bayes (WNB) classification model is employed to the transformer fault diagnosis based on the extracted magnanimous feature data. To further improve the performance of fault diagnosis method, chaotic quantumbehaved particle swarm optimization is proposed to optimize the parameters of SAE and WNB classification model, respectively. A 10 kV transformer fault diagnosis results show that the proposed RFID sensor tag can reliably collect the vibration signals, and the fault diagnosis method has a high correct rate of fault diagnosis.