With the continuous traction of Chinese government policies and the new artificial intelligence technology, the research of mineintelligence has continued to make breakthroughs in recent years. The intelligent construction of coal preparation plant as a part of intelligent mine has received great attention, among which, the intelligent control technology of coal flotation has been one of the key bottleneckshindering the intelligent construction of coal preparation plant. In this paper, the life cycle of coal slime flotation data was taken asthe main research line, the research progress of coal flotation intelligent control technology was reviewed from three perspectives: onlineprediction of coal flotation concentrate/ tailings ash content, intelligent addition of the flotation regents and intelligent decision-makingof coal flotation system, and the research tendency of coal flotation intelligent control was looked forward to the future. The online prediction of concentrate ash content is still difficult, and the single computer visual feature information of froth image is not reliable, the prediction technology of tailings ash content is relatively more reliable. The addition of flotation regents is limited by multiple flotation conditionvariables at the same time, and the adaptability and generalization ability of model performance in the entire working condition intervalneed to be further improved. The current research on flotation intelligent control technology is limited by the prediction accuracy of coal flotation concentrate/ tailings ash content, sensor detection accuracy, and agent addition accuracy. The flotation process dataset is more dimensional, making it difficult to establish a reliable knowledge base. The new generation of artificial intelligence technology represented bydeep learning can adapt to this kind of data structure. In addition, the existing flotation monitoring system only targets specific minerals,with high uniqueness. In the future, the coal flotation intelligent control system should focus on overcoming the limitations of index prediction and sensor detection accuracy, and establish a large dataset and large model of multi-coal and templated intelligent control data.