Abstract: By applying the system analysis principle and mathematical modeling technique to knowledge expression system for crop cultivation management, the fundamental relationships and quantitative algorithms of rapeseed growth and management indices to variety types, ecological environments and production levels were analyzed and extracted, and a dynamic knowledge model with temporal and spatial characteristics for rapeseed crop management was developed. Based on the utilization of the soft component characteristics, a knowledge model-based decision support system for rapeseed management (KMDSSRM) was developed on the platforms of Visual C⁺⁺. The system can be used to design pre-sowing cultivation plan and predict suitable growth regulation indices of rapeseed. The pre-sowing cultivation plan includes yield level and components, variety type, sowing date, population density and sowing rate, transplanting plan, fertilization and water management. Regulation indices include time-course leaf age, leaf area index, pod area index and dry matter weight. The knowledge model overcomes the shortcomings of traditional rapeseed cultivation pattern and expert system, such as site specific and narrow applicability, and thus provides a framework for quantitative and digital decision-making on rapeseed cultivation management.