Abstract: A method is proposed to address the strategic needs of national mission planning and demonstration in the field of deep space exploration, focusing on challenges such as the diversity and complexity of missions, the large scale of data, and difficulties in direct data utilization. The construction of an ontology model for the deep space exploration domain is carried out using a hybrid approach that combines top-down and bottom-up methodologies. In the top-down process, domain knowledge is analyzed and expert input is incorporated to build the ontology model. On this basis, a bottom-up ontology updating strategy is introduced, leveraging N-gram analysis and text semantic clustering to dynamically update the ontology through semantic recognition and processing of textual data related to deep space exploration. Structured, semi-structured, and unstructured data are integrated to build a comprehensive knowledge base for the domain, comprising 2140 entity nodes, 4608 relationships, and 9079 attributes. Based on this knowledge base, a payload configuration method is proposed to recommend payloads that meet specific scientific goals. This is achieved through correlation analysis of scientific target data within the knowledge base, combining a knowledge extraction model with a semantic similarity model to synthesize exploration object information and historical payload data. The effectiveness of this approach is validated using the Tianwen-1 mission, demonstrating its capability in supporting payload planning, scientific target analysis, and mission configuration in the field of deep space exploration.