Potassium-rich brine, an important source of liquid potassium salt, is one of the major potassium-seeking directions in the Sichuan Basin. The potassium-rich brine in Triassic carbonate formation of the eastern Sichuan has a high degree of salinity, high exploitation potential and huge resources. It is one of the key areas of potassium salt exploration in China. Aiming at the characteristics of conventional logging interpretation method to identify deep brine with slow speed and low accuracy, it was proposed to establish BP neural network model to carry out the identification and division of potassium-rich brine in Sichuan Basin. Based on the theory of BP neural network and logging interpretation methods, a neural network model with logging curves as input was built and applied to the deep brine and potassium-rich brine. The discrimination results were compared with logging data. The accuracy rate of the model reaches 85.7%,and the accuracy rate of the improved potassium-rich brine model achieves 89.1%. This study demonstrates that BP neural network has a wide application prospect in the exploration and development of potassium salt in Sichuan Basin.