Aimed at the complex heating problem of liquid hybrid bearing during operation,CFD simulation was used to analyze liquid hybrid bearing by Fluent software,and the distribution of temperature field of oil film,the average temperature and maximum temperature of the bearing in operation were obtained under different input state.On this basis,the Fluent simulation was combined with BP neural network through the orthogonal experiment,and the prediction of operating temperature of liquid hybrid bearing was carried out in random input parameters.The effects of combinational function of the rotational speed and oil pressure,oil pressure and oil temperature were analyzed.The results show that the effect of spindle speed on bearing is more significant.When the bearing runs in a high speed,the oil pressure should be raised to maintain the liquid hybrid bearing operating in a normal state.When oil pressure dropping and oil temperature rising appear at the same time,the operating temperature of liquid hybrid bearing will increase sharply,and it must be treated with the caution.By using the extensive function of BP neural networks,sample points of network training in full uniform can be obtained with small amount of samples,thereby the thermal characteristics analysis can be implemented quickly and effectively on the liquid hybrid bearing by BP neural networks.