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It is very important to improve the fault diagnosis ability to ensure the stable operation of the autonomous underwater vehicle (AUV) system. Fault classification has recently been the focus of fault diagnosis for AUV. A weighted naive Bayesian (WNB) algorithm based on information gain ratio is proposed to classify the fault patterns according to the data feature of AUV propeller system. Firstly, the prior probability and each attribute information gain ratio of AUV fault training samples are calculated. Secondly, the WNB model is built based on the information gain ratio. Then, the classification model is used to realize the fault classification by obtaining the maximum posteriori probability of the fault pattern. The Simulation results demonstrate the feasibility and effectiveness of the proposed algorithm, which has higher classification success rate, compared with naive Bayesian algorithm and the decision tree algorithm.