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Vocalization is a direct expression of poultry’s rich body information, physiological characteristics, stress response and health status, which can be used to characterize emotional health changes, physiological growth feedback, and feeding regulation with the advantages of non-invasive, non-stress and continuous monitoring. In order to make better use of audio multi-dimensional features to classify poultry vocalizations, a recognition method for laying hens’ vocalizations based on multi-feature fusion was proposed. Typical calls of laying hens such as egg laying, singing, feeding and screeching in perching system were collected and analyzed, the Mel frequency cestrum coefficient, short-time zero-crossing rate, formants and first-order difference were computed by Matlab software. The classification and recognition models of laying hens’ vocalizations were established based on genetic algorithm optimized BP neural network according to the multi-feature fusion. The results showed that the average recognition rate by this method for laying hens’ sounds of egg laying, singing, feeding and screeching was 91.9%, and the accuracies were 90.2%, 93.0%, 93.3% and 92.2%, respectively;and their sensitivities were 94.9%, 90.0%, 89.4% and 91.8%, respectively. The average accuracy and sensitivity were 92.2% and 91.5%, respectively. It was found that this recognition method of laying hens’ vocalizations based on multi-feature fusion had a higher classification accuracy and sensitivity, which could be used for automatic discrimination and classification for different livestock and poultry sounds.