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In the early stage, pig cough sound could be detected for early disease warning, and a method based on deep belief nets (DBN) was proposed to construct a pig cough sound recognition model. Pig sounds of Landrace pigs, including cough, sneeze, eating, scream, hum and shaking ears sounds were automatically recorded. The samples were preprocessed by speech enhancement algorithm based on a psychoacoustical model and speech endpoint detection algorithm based on short-time energy to reduce the noise and get useful parts of samples. Based on the dynamic time warping (DTW) algorithm, the short-time energy characteristics were scaled to a 300-dimensional short-time energy feature vector, while the 24-dimensional MFCC characteristics were scaled to a 720-dimensional MFCC feature vector. And then the 300-dimensional short-time energy feature vector and the 720-dimensional MFCC feature vector were combined to construct a 1020-dimensional vector as the input of the deep belief nets. The number of neuron of the three hidden layers were set to be 42, 17 and 7, respectively, so the pig sound recognition model based on DBN was finally designed to be 1020-42-17-7-2. The 5-fold cross validation experiment showed that recognition rate, error recognition rate and total recognition rate of the best experimental group were 94.12%, 7.45% and 93.21%, respectively. Furthermore, the first 479 principal components of 1020 dimension feature parameters were obtained by PCA dimensionality reduction. The recognition rate, error recognition rate and total recognition rate obtained better performance, and the best experimental group reached 95.80%, 6.83% and 94.29%, respectively. The result demonstrated that the DBN model was effective for the pig cough recognition.