Abstract: In order to solve the problem that a large amount of shearer operating state data cannot be processed in time, the distributed real-time prediction model of shearer operating state data based on Storm is proposed. Combined with the actual operating state data of the shearer, the Hadoop distributed storage database is used to simulate the real-time data flow of the operating state of the shearer. The Storm distributed real-time big data processing framework is used to process a large number of time series data of the operating state of the shearer. And Gate Recurrent Unit (GRU) is adopted as the prediction model to achieve real-time prediction of shearer operating state data. Combined with the threshold setting of various data, the model can realize fault warning. Taking the data of MG400930-WD electric traction shearer in fully mechanized working face as an example, eight kinds of monitoring data are used as experimental data to train and test the prediction model. The data include cutting part motor current, cutting part motor temperature, traction part motor current, traction part motor speed, height adjustment pump working pressure, height adjustment pump working speed, cooling water pressure and inverter current. The results show that the prediction model converges quickly, and the goodness of fit is above 0.9. Except for the cooling water pressure, the early warning accuracy rate of the remaining data is above 95%. The processing speed is fast and the whole early warning process is about 10 s in total, which can meet the application requirements.