Abstract: Cropping patterns describe how crops are arranged and sequenced through time, and they directly shape agricultural inputs, yields, and the associated regional climate and ecological processes. Optimizing the spatial distribution of cropping patterns, integrating agricultural intensification and sustainable development, is now a broadly endorsed objective. The Jianghan Plain is an important agricultural production base in central China in Hubei Province, characterized by intensive cultivation and diverse cropping systems. In recent years, non-grain and non-farming conversion has become increasingly prominent, accompanied by growing pressure on resources and the environment. Timely and accurate mapping of cropping patterns and their spatial optimization from food security and ecological security perspectives are essential for guiding sustainable agricultural restructuring on the Jianghan Plain. However, existing cropping pattern optimization studies have primarily focused on administrative-scale structural adjustments, leaving spatially explicit, plot-level approaches largely underexplored. This study integrated time-series remote sensing data and phenological knowledge to map the major cropping patterns on the Jianghan Plain, constructed a sustainable intensification evaluation framework from food security and ecological security perspectives, incorporating six indicators including output value per unit area, net primary productivity, crop water requirement, non-point source pollution load, and greenhouse gas emission intensity, to quantitatively evaluate the production benefits and ecological costs of cropping patterns from food security and ecological security perspectives. On this basis, through incorporating multi-dimensional indicators such as resources, environment, and socio-economic factors, a Bayesian Network for cropping patterns optimization was built to explore the cropping patterns optimization results under three scenarios: production, conservation, and trade-off. The results indicated that: (1) There were significant differences in the production benefits and ecological costs of cropping patterns. There were significant differences in the production benefits and ecological costs among cropping patterns. In terms of production benefits, double-cropping paddy rice achieved the highest score (0.91), followed by winter wheat-paddy rice (0.87) and winter rapeseed-paddy rice (0.84). Regarding ecological costs, winter wheat-soybean and winter rapeseed-soybean exhibited the lowest ecological pressure (both 0.37), while double-cropping paddy rice incurred the highest ecological cost. (2) The trade-off scenario, which balances food security with ecological conservation, was identified as the optimal solution. Winter wheat-paddy rice, winter rapeseed-paddy rice, winter wheat-soybean, and double-cropping paddy rice, which achieved relatively high comprehensive expected effects, were identified as viable alternatives for cropping pattern optimization on the Jianghan Plain, with their combined proportion adjusted from the current 11.9% to 32.6%. Spatially, double-cropping paddy rice is best suited to the southeastern Jianghan Plain, winter wheat-soybean is recommended for the central region, winter wheat-paddy rice is optimal for the southwestern area and along the Han River, and winter rapeseed-paddy rice is most appropriate for higher-altitude areas distant from rivers. This optimization ensures agricultural output while maximizing regional ecological positive benefits, achieving sustainable intensive agriculture. The proposed Bayesian Network framework enables plot-level spatial optimization of cropping patterns under multivariate and multi-scenario conditions, offering a technically feasible approach to optimizing cropping patterns from the dual perspectives of food security and ecological security. This approach enhances cropland use efficiency and provides actionable insights to optimize cropping patterns in comparable regions, while charting a viable pathway toward green, sustainable agricultural development and national food security.