In order to discover the hidden troubles of WSN nodes in time and accurately know the running status of WSN, this paper uses the attribute reduction algorithm of rough set theory (RS for short) to reduce the fault attributes of WSN nodes, and reconstructs the training sample data set with the optimal fault attribute decision table as an input to the Extreme Learning Machine (ELM) neural network. In this way, a data-driven fault diagnosis model of WSN nodes is established. The input weights and hidden layer thresholds of the ELM neural network are optimized through Crow Search Algorithm (CSA) to alleviate the unstable output and improve the low classification accuracy of the ELM model caused by the random generation of network parameters. Simulation analysis of the RS-GA-ELM model is carried out. The results show that the RS-GA-ELM model can keep efficiently diagnose faults in data sets with different reliability, which meets the needs of fault diagnosis of WSN nodes.