Precise crop yield estimation is crucial for maintaining national food security. Crop growth models, statistical regression methods, and deep learning methods are among the numerous methods available for estimating crop yields. Deep learning, with its robust feature extraction capability and data-driven features, has propelled agriculture forward by leaps and bounds, and one of the applications oriented to national needs is the agricultural application of deep learning-based AI technology. However, as research and applications deepen and grow, the limitations of data-driven methodologies have emerged, such as difficulties in migrating reuse, reliance on samples, and poor interpretability. The organic integration of knowledge in various forms of expression and deep learning is the way to realize the dual drive of knowledge and data, and the integration of the two has not been well investigated. Therefore, we proposed a knowledge-guided deep learning (KGDL) framework that integrated knowledge with CNN and BiLSTM networks from both feature-level and network-level perspectives for winter wheat yield estimate in the Guanzhong Plain from 2007 to 2021. Our results showed that incorporating feature-level and network-level knowledge significantly enhanced wheat yield estimates, achieving R² = 0.73, RMSE = 447.12 kg/ha, and MAPE = 8.72%, reducing uncertainty by 109.88 kg/ha for the high-yield dataset, 81.73 kg/ha for the medium-yield dataset and 55.34 kg/ha for the low-yield dataset. Furthermore, the KGDL model, driven by both knowledge and data, resulted in a gradual increase in the coefficients between the output and the target yield on a layer-by-layer basis, with the average coefficients increasing from 0.35 to 0.58. Overall, the proposed framework shows promising application prospects for deep learning-based crop yield estimation, which can provide important technical support for promoting precision production in regional agriculture.