Underwater acoustic target recognition is a research and development hot spot in many countries in recent years.How-ever,it is difficult to collect underwater acoustic targets,resulting in the sample data insufficient,which seriously affects the recogni-tion efficiency of neural network and the level and performance of automatic recognition equipment.Therefore,an optimization meth-od of underwater acoustic target classification model based on the sample expansion network is proposed.Through building the sample expansion network reconstructed by the mask,the unlabeled data is fully utilized to train the model,learn the global high-dimensional features of the samples,and then generate the samples to be added to the subsequent recognition model training.Based on the results of two experiments,the average accuracy of target classification model improves from 76％to 80％,with its maximum accuracy im-proving from 88％to 96％.Experimental results show that this method improves the accuracy,convergence speed and stability of the classifier.
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