Intelligent structural design in the scheme phase is an essential component of intelligent construction. Existing studies have proposed the deep neural network-based framework of intelligent generative structural design, intelligent design algorithms, and design performance evaluation methods for shear wall structures, which have developed intelligent structural design methods from data-driven to physics-enhanced data-driven. However, little detailed design performance comparison of data-driven and physics-enhanced methods under different design conditions is conducted. Furthermore, the relationship between the computer vision-based and mechanical analysis-based evaluation methods are still unclear, resulting in difficulties in effectively guaranteeing the rationality of the computer vision-based evaluation methods. Hence, in this study, the comparative analysis of data-driven and physics-enhanced intelligent design methods is conducted by algorithm comparison and case studies; and the consistent relationship between computer vision-based and mechanical analysis-based evaluation methods is validated. The comparison results reveal that data-driven methods are more prone to be limited by the quality and quantity of training data. In contrast, the physics-enhanced data-driven design method is more robust under different design conditions and is little affected by the data-caused limitation. Moreover, the rationality threshold of the computer vision-based evaluation index (SCV) is 0.5, corresponding to a difference in the mechanical performance of approximately 10%.