Abstract: The solid backfilling mining method has great advantages in handling coal-based solid waste and controlling surface subsidence, but its low backfilling efficiency, long succession time and high labour intensity constrain the development of green backfilling mining. Aiming at the endogenous driving force of solid backfilling technology upgrading, the urgent demand of industry development and the inevitable trend of mine intelligent construction, the solid intelligent backfilling method analysed by video AI algorithm is proposed. Initially, this paper examines the essence and challenges of intelligent solid backfilling methods, establishes the system framework for analyzing the intelligent solid backfilling mining method using video AI algorithms, elucidates the operational principles and implementation process of the video AI algorithm, and outlines the capabilities that can be achieved through this algorithm. It analyzes the influencing factors on key backfilling equipment under different geological conditions. The hydraulic support framework modeling was conducted using Creo to simulate its movement in various working conditions; corresponding control criteria and paths were provided, along with a designed control algorithm flow for key backfilling equipment across different processes. Based on the characteristics of video algorithms and their pros and cons, an image recognition algorithm is initially chosen; after training and adjusting the model, optimal algorithms and corresponding parameters are determined. Through an analysis of application effects on an coal mine fill surfaces, it is found that SVM evaluation indices outperform other algorithms, indicating excellent performance in discriminating working conditions with high accuracy and reliability. The research can realize the identification and regulation of abnormal working conditions of key backfilling equipment mechanism, improve backfilling efficiency, identify the positional parameters of the mechanism, and display the effect of backfilling space tamping, which can provide theoretical guidance for the research, development and application of solid intelligent backfilling and mining technology analyzed by video AI algorithm.