Deep learning technology has achieved remarkable results in computer vision, speech recognition, and decision-making in recent years, so in theory, it is feasible to apply it to target recognition and decision-making in unmanned ground vehicles (UGVs). The following briefly introduces several key aspects, and also points out technical and ethical issues that require special attention: 1. Target recognition capability • Image and sensor data processing: Deep learning, especially convolutional neural networks (CNNs), excels in image classification, target detection, and semantic segmentation. Unmanned vehicles can use installed sensors such as cameras and lidars, combined with deep learning models, to quickly detect and identify targets in complex battlefields or environments. • Data requirements and training: Achieving high-precision target recognition requires a large amount of diverse data to train the model. The richness and quality of the data directly determine the robustness of the model in various practical environments. 2. Attack decision-making capability • Decision-making and strategy optimization: In recent years, deep reinforcement learning (DRL) has been used to solve decision-making problems such as path planning and resource allocation. In theory, such technologies can be used to allow unmanned vehicles to undergo extensive training in simulated environments, so that they can learn how to choose the attack timing and the best decision in specific scenarios. • Real-time and security: In actual scenarios, decision-making systems not only need to respond in real time, but also must be fault-tolerant and anti-interference. The formulation of attack decisions must especially consider the potential deceptive interference of opponents (such as adversarial samples) and the uncertainty of complex environments. 3. Comprehensive challenges and risks • Security and robustness: Deep learning models may be affected by adversarial attacks, data drift and other issues. In high-risk areas (such as military use), any system failure may have serious consequences, so additional safety verification, redundancy design and continuous monitoring are required. • Ethics, law and responsibility: The application of deep learning to unmanned vehicles with attack decisions has caused major ethical and legal disputes. For example, how can automated weapon systems ensure compliance with international humanitarian law and ethical standards when making lethal decisions, and to what extent should "human-in-the-loop" supervision be maintained? These issues need to be rigorously evaluated before system development and deployment, and comply with legal regulations. • Human-machine collaboration principle: The current mainstream concept tends to let unmanned systems rely on advanced technology for information collection, preliminary identification and recommended decision-making, but retain the final decision-making power to human commanders, so as to ensure higher judgment accuracy and accountability mechanism when dealing with complex, uncertain or critical situations. 4. Research and application prospects • Experimental and simulation platforms: Many research institutions and military laboratories have tested the application of deep learning in unmanned systems on simulation platforms, from target detection to tactical decision-making. However, to transform laboratory results into actual combat environments, challenges such as data generalization, real-time processing and cross-domain adaptation need to be solved. • Interdisciplinary cooperation: The realization of these technologies involves not only the design of deep learning models, but also the collaborative work of systems engineering, sensor fusion, control algorithms, and ethics and policy making. In summary, deep learning technology has obvious potential to enhance the target recognition and decision-making capabilities of unmanned ground vehicles, especially in improving information processing speed, making complex scene judgments and realizing autonomous learning. However, any attempt to apply this technology to the field of attack decision-making must be fully evaluated and strictly controlled in terms of technical reliability, security, ethical legitimacy and international regulations. Ensuring that potential safety issues and legal liabilities are fully considered before actual deployment is a key prerequisite for future development in this area.