  With the rapid advancement of technology, the recognition of underwater
acoustic signals in complex environments has become increasingly crucial.
Currently, mainstream underwater acoustic signal recognition relies primarily
on time-frequency analysis to extract spectral features, finding widespread
applications in the field. However, existing recognition methods heavily depend
on expert systems, facing limitations such as restricted knowledge bases and
challenges in handling complex relationships. These limitations stem from the
complexity and maintenance difficulties associated with rules or inference
engines. Recognizing the potential advantages of deep learning in handling
intricate relationships, this paper proposes a method utilizing neural networks
for underwater acoustic signal recognition. The proposed approach involves
continual learning of features extracted from spectra for the classification of
underwater acoustic signals. Deep learning models can automatically learn
abstract features from data and continually adjust weights during training to
enhance classification performance.
