N2 and CO2 fixation is an environmentally friendly waste‐to‐energy technology that can replace tedious and demanding industrial urea productive processes. Here, VN‐Cu3N‐300 catalysts with tip and vacancy structures are designed for the coactivation of multiphase reactants in the urea electrosynthesis process. Under environmental conditions, the urea yield is 81 µg h−1 cm−2, this is the first report of high area active electrocatalyst, and the corresponding Faraday efficiency is able to reach 28.7%. The full‐cell electrolysis exhibits good stability, providing a current density of 0.7 mA cm−2 at 2.1 V. The electrolyte after continuous electrolysis for 48 h is subjected to being evaporated and recrystallized, and it is determined by 1H NMR that the purity of urea can reach 100%. Comprehensive analysis shows that the local electric field formed by the tip effect can effectively promote the adsorption and activation of CO2. The presence of surface nitrogen vacancies promotes the formation of *NN* intermediates, ensuring CN coupling, and also optimizing the dissociation process of water, providing a proton supply for the synthesis of urea. Thus, the rate‐determining step is altered and the formation of urea is ensured.

The classification of surrounding rock quality is critical for the dynamic construction and design of tunnels. However, obtaining complete parameters for predicting the surrounding rock grades is always challenging in complex tunnel geological environment. In this study, a new method based on Bayesian networks is proposed to predict the probability for the classification of surrounding rock quality of tunnel with incomplete data. A database is collected with 286 cases in 10 tunnels, involving nine parameters: rock hardness, weathering degree, rock mass integrity, rock mass structure, structural plane integrity, in-situ stress, groundwater, rock basic quality, and surrounding rock level. Moreover, the Bayesian network structure is built using the collected database and quantitatively verified by strength analysis. Then, the accuracy, precision, recall, F-measure and receiver operating characteristic (ROC) curves are utilized for model evaluation. The average values of accuracy, precision, recall, F-measure, and area under the curve (AUC) are approximately 89.2%, 91%, 92%, 91%, and 0.98, respectively. These results indicate that the established classification model has high accuracy, even with small sample size and imbalanced samples. Ten additional sets of tunnel cases (incomplete data) are also used for verification. The results reveal that compared with the traditional Q-system (Q) and rock mass rating (RMR) classification methods, the proposed classification model has the lowest error rate and is capable of using incomplete data to predict sample results. Finally, sensitivity analysis suggests that the rock hardness and rock mass integrity have the strongest impact on the quality of tunnel surrounding rock. Overall, the findings of this study can serve as a useful reference for future rock mass quality evaluation in tunnels, underground powerhouses, slopes, etc.