The invention discloses a fan fault detecting method based on a depth belief network model. The fan fault detecting method comprises that a fan flow forecast model is established by using depth beliefnetwork, state parameters related to the fan flow are selected to be used as the input variable of the model, and the flow of a fan is forecasted. The depth belief network is formed by staking multilayer continuous type limit Boltzmann machines, and an adaptive step length method is used to accelerate the algorithm training process. In addition, residual distribution characteristics are calculated according to a sliding window, when the mean value or standard deviation of the residual exceeds the threshold, alarm is issued. By means of the fan fault detecting method based on the depth beliefnetwork model, the flow of the fan can be accurately forecasted, meanwhile, the abnormal working state of the fan can be detected, and fault detection on the fan is achieved.