Deep learning algorithms can be used to improve the autonomous search and attack capabilities of torpedoes in the following ways: 1. **Target recognition and classification**: - Use convolutional neural networks (CNNs) to recognize and classify underwater targets. Through training with a large amount of labeled data, torpedoes can more accurately identify targets such as submarines and ships, improving the recognition rate. 2. **Environmental perception**: - Use deep learning models to process data from sensors such as sonar and cameras to achieve real-time perception and mapping of the underwater environment. Use recurrent neural networks (RNN) or long short-term memory networks (LSTM) to process time series data to better understand the dynamically changing ocean environment. 3. **Path planning**: - Use reinforcement learning algorithms to optimize the path planning of torpedoes in complex ocean environments. Through simulation training, torpedoes can learn to choose the best path when facing various obstacles, avoid enemy detection systems and effectively approach targets. 4. **Autonomous decision-making**: - Combining game theory with deep reinforcement learning, an intelligent decision-making system is designed to enable torpedoes to make autonomous decisions in complex situations, such as choosing the timing and method of attack. Through training, torpedoes can make trade-offs among different strategies to increase the success rate of attacks. 5. **Multi-target tracking**: - Apply a multi-target tracking algorithm based on deep learning to monitor and track multiple targets in real time. This can help torpedoes quickly select priority attack targets when facing multiple threats. 6. **Data fusion and reduction of false alarm rate**: - Fusion of data from different sensors (such as infrared, sonar, radar, etc.) to improve the accuracy of target detection. Deep learning can help extract features from data and reduce false alarms and missed alarms. 7. **Simulation training**: - Conduct deep learning training in a virtual environment to simulate a variety of marine environments and attack scenarios to improve the adaptability and actual combat response capabilities of torpedoes. Through these methods, deep learning algorithms can significantly improve the autonomous search and attack capabilities of torpedoes, and enhance their survival and combat effectiveness in complex battlefield environments.