Abstract: Intelligent coal mining is an inevitable course for the high quality development of the coal industry. The intellectualization development of longwall top coal caving (LTCC) face lags behind the fully mechanized face or heading face. The intelligent top coal caving is the core technology of achieving an intelligent top coal caving mining. However, there are some problems, such as low illuminance, narrow space, high concentration dust, overlapping coal and rock, interference of acoustic vibration signal and rock parting, which seriously restrict the development of intelligent top coal caving. On the basis of exploring a variety of recognition methods based on image, sound and vibration, an image based recognition of withdrawn coal in LTCC face (IB-LTCC) was developed. Accurate and rapid recognition of rock mixed ratio and harsh environment adaption are two main technical problems that need to be solved for IB-LTCC. In order to solve the former problem, the rock mixed ratio was refined into projection area rock mixed ratio, surface volume rock mixed ratio and inner volume rock mixed ratio. The lightweight rock recognition and boundary measurement model used for LTCC face was established, which achieved an accurate and rapid recognition of projection area rock mixed ratio. Two types of three dimensional blocks reconstruction methods including fast freehand brushwork reconstruction and accurate reconstruction were put forward. The relationship between three dimensional morphological characteristics and two-dimensional morphological characteristics of coal and rock blocks was studied, and the packing mechanics of coal and rock blocks on the rear armored face conveyor (RAFC) were revealed. “Surface to Inside” (S2I) protocol to measure rock mixed ratio (RMR) with high accuracy was put forward, so as to realize the transparency of coal flow and achieve the purpose of high precision measurement of rock mixed ratio. On the other hand, aiming at the problem of adaptation to harsh environment such as low illuminance and high concentration dust, stereo vision based illuminance measurement used for intelligent lighting was proposed to provide the optimal lighting environment for image acquisition in real time. The intelligent image collection system was developed based on human bionics and edge AI technology. Single channel Retinex dust removal algorithm based on frequency domain prior and an enhanced dust removal algorithm based on space frequency domain were proposed to continuously provide some high-quality images for image recognition. The“three in one”rock parting intelligent recognition technology was developed, which accurately recognized the rock parting that may appear in the coal caving process, reduce the mis recognition and mis operation caused by the drawing of rock parting. The IB-LTCC can improve the intelligent level of coal caving process, improve resource recovery, decrease rock mixed ratio and ensure the safe production of coal mine. The scientific application of research achievement will be helpful for achieving an intelligent LTCC with high quality.