Abstract: Under the background of scarce scientific and technological innovation factors, promoting more agribusinesses to optimize their factor inputs toward higher efficiency is a key issue in accelerating the modernization of agricultural science and technology. This study identifies the innovation factor that contributes most to reducing factor misallocation as the “key factor” and classifies listed agribusinesses based on the number and type of their key factors. Accordingly, a gradient optimization strategy that prioritizes key factors is proposed for different categories of enterprises. Methodologically, this paper improves the traditional Hsieh & Klenow (HK) model by incorporating the characteristics of agricultural innovation cycles, making it better suited to the long-term nature and heterogeneity of agribusinesses, and uses it to measure factor misallocation. Furthermore, a super-efficiency SBM (SBM-VAR-SDEA) model that accounts for undesirable outputs and allows for variable returns to scale is constructed to evaluate the efficiency of technological innovation and the slack in input-output combinations. The results show that adjusting the structure of innovation factor inputs has a positive effect on most agribusinesses. In the optimization process, key factors should be prioritized and adjusted to their optimal input levels, while non-key factors should be adjusted proportionally based on their original structure. This strategy applies to both single and multiple key factor scenarios. The optimization results indicate that key factor inputs are effectively compressed, while non-key inputs and undesirable outputs do not increase, thus improving overall input efficiency. The gradient optimization method proposed in this study provides both theoretical support and practical pathways for enhancing the efficiency of innovation factor utilization in agribusinesses and offers important policy implications for advancing the modernization of agricultural science and technology in China.