Underwater acoustic target recognition remains a formidable challenge in underwater acoustic signal processing. Current target recognition approaches within underwater acoustic frameworks predominantly rely on acoustic image target recognition models. However, this method grapples with two primary setbacks; the pronounced frequency similarity within acoustic images often leads to the loss of critical target data during the feature extraction phase, and the inherent data imbalance within the underwater acoustic target dataset predisposes models to overfitting. In response to these challenges, this research introduces an underwater acoustic target recognition model named Attention Mechanism Residual Concatenate Network (ARescat). This model integrates residual concatenate networks combined with Squeeze-Excitation (SE) attention mechanisms. The entire process culminates with joint supervision employing Focal Loss for precise feature classification. In our study, we conducted recognition experiments using the ShipsEar database and compared the performance of the ARescat model with the classic ResNet18 model under identical feature extraction conditions. The findings reveal that the ARescat model, with a similar quantity of model parameters as ResNet18, achieves a 2.8% higher recognition accuracy, reaching an impressive 95.8%. This enhancement is particularly notable when comparing various models and feature extraction methods, underscoring the ARescat model’s superior proficiency in underwater acoustic target recognition.

Due to its capacity to unveil the dynamic characteristics of time series data, entropy has attracted growing interest. However, traditional entropy feature extraction methods, such as permutation entropy, fall short in concurrently considering both the absolute amplitude information of signals and the temporal correlation between sample points. Consequently, this limitation leads to inadequate differentiation among different time series and susceptibility to noise interference. In order to augment the discriminative power and noise robustness of entropy features in time series analysis, this paper introduces a novel method called Tsallis entropy-based complexity-improved permutation entropy casualty plane (TC-IPE-CP). TC-IPE-CP adopts a novel symbolization approach that preserves both absolute amplitude information and inter-point correlations within sequences, thereby enhancing feature separability and noise resilience. Additionally, by incorporating Tsallis entropy and weighting the probability distribution with parameter q, it integrates with statistical complexity to establish a feature plane of complexity and entropy, further enriching signal features. Through the integration of multiscale algorithms, a multiscale Tsallis-improved permutation entropy algorithm is also developed. The simulation results indicate that TC-IPE-CP requires a small amount of data, exhibits strong noise resistance, and possesses high separability for signals. When applied to the analysis of heart rate signals, fault diagnosis, and underwater acoustic signal recognition, experimental findings demonstrate that TC-IPE-CP can accurately differentiate between electrocardiographic signals of elderly and young subjects, achieve precise bearing fault diagnosis, and identify four types of underwater targets. Particularly in underwater acoustic signal recognition experiments, TC-IPE-CP achieves a recognition rate of 96.67%, surpassing the well-known multi-scale dispersion entropy and multi-scale permutation entropy by 7.34% and 19.17%, respectively. This suggests that TC-IPE-CP is highly suitable for the analysis of complex time series.