Abstract: In view of the problems of effective intelligent and full coverage early warning technology，and the serious threat from floor water disaster when mining the deep coal from Gourp A in Huaihe Energy Group，the technical idea of guiding the prevention and control of water inrush in mining area was proposed.The big data intelligent early-warning cloud platform was built from the multi-source information based on the real-time monitoring of hydrology，water source，and floor failure.Based on the engineering background of the 1612A working face of Group A in Zhangji coal mine，the internet of things of hydrological monitoring data transmission system was constructed to observe the changes of water source，water level，water pressure，water quality，water temperature，and other parameters，which was used for real-time data collection，data transmission，data analysis，and processing.The micro-seismic monitoring internet of things was built for estimating failure depth of the floor.The sensor can pick up the floor failure signal and transmit it to the ground data signal processing terminal through the optical fiber ring network of the mine.The real-time monitoring and inversion of the floor failure，and the distribution of the water conducting fracture channel can be carried out.Taking various factors of mining，geology，and monitoring data into account，an early-warning model based on neural network and in-depth learning was established.The main control index and evaluation index of coal mine safety evaluation were determined，and machine learning training was carried out for the model by collecting a large amount mine data nationwide.A big data early-warning cloud platform system for water inrush disaster from coal mine floor was established，and the system integrates five modules:data collection，management configuration，equipment monitoring，central control panel，multi-dimensional analysis，and fault early warning.The seamless connection was realized by network integration and data integration technologies.The learned early-warning model was embedded in the system.Based on the integrated multi-source data，the risk assessment and early warning of water inrush from the mine floor were carried out，and the decision-making information was released in real time through mutual feed analysis with micro-seismic data.At last，five groups of data of coal mines in Huainan and other mining areas were selected to verify the model.The evaluation and analysis were carried out in the 1612A working face.The results show that the calculation and prediction results of chosen mines were reasonable，and the probability of water inrush disaster in the Zhangji coal mine was small.There was no early warning information in the monitoring period，which confirmed the feasibility of the application of big data cloud platform in the prevention and control of coal mine floor water disaster.It provides a new technical support for monitoring and early warning of mine floor water disaster in mining deep coal in Huaihe Energy Group.