As deep space exploration missions continue to advance toward higher autonomy，longer operational duration，and more complex environments，the traditional task paradigm dominated by ground-based teleoperation can no longer satisfy the requirements for real-time，reliable，and efficient operations in lunar，Martian，and farther deep-space scenarios. Under conditions such as significant communication delay，limited prior environmental knowledge，strict onboard resource constraints，and high trial-and-error cost，future deep space platforms are expected to move beyond single-command execution toward integrated autonomous operation. In this context，embodied intelligence is gradually emerging as an important research direction for deep space exploration. The reason is that it provides a unified framework for coupling environmental perception，autonomous decision-making，and physical execution，which improves the adaptability and autonomy of exploration systems in long-duration extraterrestrial missions. This study systematically reviews recent progress in intelligent methods for deep space exploration from the perspective of embodied intelligence. Focusing on two central challenges—limited perception capability in highly unstructured extraterrestrial environments and insufficient autonomous decision-making capability under long-horizon，high-risk，and resource-constrained conditions—the study reviews recent advances in deep space visual perception and deep space embodied intelligence，and summarizes representative datasets，typical tasks，and evaluation protocols. For visual perception，the survey first reviews traditional image processing-based methods，including stereo terrain reconstruction，visual odometry，photogrammetric modeling，and large-scale localization. These methods established the technical foundation for early planetary exploration because they are grounded in explicit geometric constraints and therefore offer strong interpretability and engineering reliability. However，their effectiveness is increasingly challenged by weak textures，repeated patterns，severe illumination changes，occlusion，and long-range cumulative drift in lunar and Martian environments. The study then examines deep learning-based visual perception methods，which cover terrain semantic classification and segmentation，cross-domain adaptation，and multimodal terrain understanding. Compared with traditional geometry-driven approaches，deep models provide stronger semantic expressiveness and improved environmental understanding in complex planetary scenes. At the same time，their performance still depends heavily on data coverage，annotation quality，training strategy，and sim-to-real consistency，while onboard deployment is constrained by limited computation and energy budgets. From this perspective，the development of deep space perception is gradually shifting from closed-dataset recognition accuracy toward robust representation，long-term adaptability，and task-oriented environmental understanding. From the perspective of embodied intelligence，the study categorizes deep space tasks into three major classes： autonomous visual navigation，autonomous mission planning，and autonomous scientific operation. On this basis，the methodological evolution of deep space embodied intelligence can be viewed in three stages. The first stage is represented by teleoperation-centered control models，in which planning and key decisions are mainly completed on the ground，while the onboard system executes predefined instructions with limited rule-based local autonomy. Although this paradigm has been essential for ensuring mission safety and controllability，its strong dependence on human intervention limits its capability to support future long-duration missions in uncertain environments. The second stage is characterized by deep-learning-enhanced local autonomy. In this stage，learning models are embedded into specific modules of conventional engineering pipelines to improve terrain understanding，candidate path evaluation，risk estimation，target selection，and onboard data screening，while the overall decision and control chain remains largely interpretable and verifiable. The third stage involves multimodal large models and vision-language-action systems，which introduce higher-level cognitive capability into deep space systems. By unifying multimodal observations，state information，and task instructions in a shared semantic space，these methods enable cross-modal reasoning，task decomposition，dynamic replanning，and long-horizon decision organization. The survey argues that such models are promising for future high-autonomy missions. However，under current engineering conditions，they are more suitable as high-level cognition and task-orchestration modules than as direct substitutes for low-level planning and control. The study further reviews datasets and evaluation methods for visual perception and embodied intelligence. Compared with terrestrial benchmarks，publicly available deep space datasets remain limited in scale，annotation richness，and task diversity. For embodied intelligence，the challenge is even more fundamental，considering that datasets must support temporality，interaction，and closed-loop evaluation by including not only observations but also pose，action，state，and feedback information. As a result，high-fidelity simulation environments and digital twin-style benchmarks currently play an important role in supporting reproducible system evaluation. Correspondingly，evaluation protocols are also evolving from static recognition metrics toward task-level criteria involving safety，effectiveness，efficiency，robustness，and long-horizon reliability. Based on the abovementioned survey，the study discusses several future directions for deep space embodied intelligence，including perceptual generalization in strongly unstructured environments，trustworthy autonomous decision-making，system-level collaboration across perception and control modules，and lightweight deployment on resource-constrained platforms. In summary，deep space embodied intelligence is emerging as a system-level intelligence problem shaped by extreme extraterrestrial environments，long-duration mission chains，strict resource limitations，and high reliability requirements. Its future advancement depends not only on improving individual algorithms but also on establishing an integrated framework that unifies perception，decision-making，execution，validation，and deployment for real deep-space missions. This survey aims to provide a structured reference and clearer analytical perspective for the further development and application of autonomous intelligence technologies in deep space exploration.