In longwall top coal caving (LTCC), the resource recovery ratio of the working face is directly determined by the top coal recovery ratio. An investigation of the evolution of top coal drawing characteristics and revealing the evolution of top coal drawing parameters is necessary when providing guidance for caving parameter selection and improving the top coal recovery ratio. Based on in-situ measurements of the size distribution of caved top coal blocks in Wangjialing coal mine, a finite difference method (FDM)–discrete element method (DEM) coupled method was applied to establish a “continuous–discontinuous” numerical model and the process from the first coal drawing to the common coal drawing was simulated with 17 separate working face advances. The evolution of the drawing body (DB), loose body (LB), and top coal boundary (TCB) was obtained. The results show that, the evolution of parameters of DB such as shape and size, drawing amount, length and deflection angle of the long axis of the profile ellipsoid tended to decrease first, then increase, decrease again, and finally stabilise; the increment of the LB advance coal wall distance and the coal pillar distance was close to 0 m in the common coal drawing stage, while width increment of the LB was close to the drawing interval (0.865 m). The TCB formed after each coal drawing round was fitted based on the improved “Hook” function. The evolution of height and radius of curvature of TCB’s stagnation point was analysed. This was divided into three stages: the first (first to third drawing rounds) was the initial mining influence stage, the second (fourth to ninth drawing rounds) was the transitional caving stage, and the third (after tenth drawing round) was the common coal drawing stage.

China has made notable advancements in the intelligent construction of coal mines. However, for longwall top coal caving (LTCC) mining faces, a key obstacle impeding the intelligent transition of the coal-cutting process is automated control. This paper focuses on the aforementioned issue and comprehensively considers the pre-, intra-, and post-coal-caving stages. In this work, diverse detection and monitoring technologies are integrated at various stages through a computer platform, facilitating the construction of an automated coal caving control system with self-perception, self-learning, self-decision-making, and self-execution capabilities. Key technologies include ground-penetrating radar-based top coal thickness detection, inertial navigation-based shearer positioning, tail beam vibration-based identification of coal and gangue, and magnetostrictive sensor-based monitoring of the tail beam and insert plate attitude. In this study, the 12309 working face of the Wangjialing Coal Mine was experimentally validated, and the efficacy of the aforementioned key technologies was assessed. The results demonstrated that the control requirements for automated coal caving are satisfied by the maximum errors. Automatic regulation of coal caving was realized through the implementation of this system, thereby facilitating initiation and cessation and yielding promising experimental outcomes. Overall, this system offers practical insights for intelligent construction in current LTCC mining faces and the sustainable development of coal resources.