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Aiming at the problem that wireless sensor network (WSN) nodes are prone to failure, a fault classification method for WSN based on improved deep forest is proposed. Deep forest is an integrated learning method based on forest. Its input is multidimensional feature vector, which is processed by the two main components of multi-granularity scan and cascade forest. The multi-granularity scan enhances the ability of data representation by processing the relationship between data, and the cascade forest is used for classification or prediction. After optimizing the dimension problem caused by the increase of layers in cascaded forest, the algorithm is applied to fault classification to improve the accuracy of fault diagnosis. At the simulation stage, the proposed algorithm was compared with deep neural network (DNN) and support vector machine (SVM) algorithms. The results show that this algorithm can accurately identify different fault types, and the identification of damage fault and power fault has reached the highest accuracy, the comprehensive average accuracy of 98.4%. The identification of offset fault, drift fault and communication fault is slightly lower than that of convolutional neural network (CNN) algorithm, but the algorithm can better meet the needs of practical engineering in terms of comprehensive training time and parameter adjustment.