The invention relates to a method for rapidly detecting the potassium content of tobacco leaves based on electronic nose-artificial neural network. The method comprises the following steps: 1, collecting several tobacco leaf samples in different producing areas, preprocessing the tobacco leaf samples, and carrying out electronic nose scanning to obtain electronic nose data of every tobacco leaf sample; 2, detecting the potassium content of every tobacco leaf sample by adopting flame photometry; 3, carrying out dimension reduction on the electronic nose data by adopting partial least squares to obtain the dimension reduction data of every tobacco leaf sample; 4, establishing a rapid forecasting model of the potassium content of tobacco leaves by adopting an artificial neural network algorithm with the dimension reduction data of every tobacco leaf sample as an independent variable and the potassium content of every tobacco leaf sample as a dependent variable; and 5, rapidly forecasting the potassium content of a tobacco leaf kind to be detected according to the established rapid forecasting model of the potassium content of tobacco leaves and the electronic nose data of the tobacco leaf kind to be detected. The method has the advantages of simplicity, rapidness, low cost, comprehensive and accurate data, no pollution and simple test.