Abstract: To realize fast and accurate measurement of grain moisture with low cost, the miniaturized channel state information（CSI） acquisition equipment was used for grain moisture detection. Two feature selection algorithms of random forest and principal component analysis were adopted to extract the feature subcarriers of the CSI amplitude index, and the ten kinds of grain moisture were classified based on the selected feature subcarriers. Considering that the application in the mobility scene was limited by power consumption and arithmetic power, the breadth learning system with simple structure, fast operation speed and low arithmetic power requirement was selected for processing CSI data and was compared with the traditional convolutional neural network（CNN） in terms of accuracy and training time. The enhancement nodes of the broad learning system（BLS） were dynamically increased. The experimental results show that the principal component analysis（PCA） algorithm maximally eliminates the redundant information in the CSI data. Compared with the CNN, the BLS can achieve faster speed and better accuracy. Therefore, the PCA-BLS combination achieves the best classification results. Increasing the number of enhancement nodes can increase the training time, but the recognition accuracy is improved to some extent.