Abstract: Rock blasting technology is crucial for national economic development, particularly resource extraction and infrastructure construction. This paper explores the constitutive relationships in rock blasting, proposing three evolutionary stages: constitutive relationships 1.0, 2.0, and 3.0. These stages focus on contradictory relationships, energy balance, and minimum action theory, respectively. Constitutive relationship 1.0 centers on the interaction between rock and explosive impact loads, emphasizing the offense–defense dynamic. It analyzes the dynamic mechanical response of rock materials under explosive and impact loads, highlighting the conflict and balance between the explosive force and the rock resistance. This stage provides fundamental insights into the behavior of rock materials under high-stress conditions. Constitutive relationship 2.0 approaches the problem from an energy perspective. It treats rocks with significant anisotropy, joints, fractures, bedding, and cavities as complex structural materials. This stage studies the dynamic equilibrium between load input energy and the energy required to destroy the material. By understanding the relationship between structural strength, input energy, dissipated energy, and releasable strain energy, researchers can better predict the response of complex rock structures to explosive loads and improve blasting efficiency. Constitutive relationship 3.0 examines the propagation laws of stress waves under explosive loads and their relationship with medium damage effects. This stage focuses on optimizing energy propagation paths based on the minimum action theory, aiming to maximize the effectiveness of the explosive force while minimizing unwanted damage to the surrounding rock mass. These theories not only reveal the mechanical behavior of rocks under different load conditions but also provide a theoretical basis for optimizing blasting design and improving blasting outcomes. In addition to these theoretical advancements, the integration of artificial intelligence and big data technologies offers a new approach to managing and predicting rock material performance. This paper proposes the concept of rock material engineering genes, which involves establishing a rock gene library to systematically manage the physical and mechanical parameters of rocks. By constructing performance prediction models using advanced data analytics and machine learning algorithms, this approach enhances the accuracy of predicting how different rock types will respond to various engineering applications. Such a comprehensive database has significant implications for resource extraction, geological disaster prevention, and infrastructure construction. The rock material gene library is expected to play an increasingly important role in mineral resource development, geological disaster prevention, and infrastructure construction, thereby promoting the development and application of engineering technology. Its integration with traditional blasting techniques can lead to more efficient and safer methods of rock blasting, ultimately contributing to the advancement of engineering practices and economic development of regions dependent on these technologies. This holistic approach underscores the importance of continued research and technological innovation in rock blasting and material science.