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A fully neural network enhanced fault warning and detection algorithm is proposed to address the problems of low fault classification detection rate, long time consumption, and poor control of node energy consumption in existing WSNs fault detection algorithms. The neuron nodes of the full neural network are connected with the nodes in the adjacent layers to form a deep network structure with strong fault set training function. The sigmoid function with better smoothness is selected as the activation function of the model, and the weight threshold between the adjacent two hidden layers is reasonably adjusted based on the perceptron to reduce the training loss of the model; Using the Adam optimization algorithm to suppress the gradient expansion and vanishing of the model, and eliminate data redundancy generated during training, reducing the false warning generated by fault data training. The experimental results show that the overall fault detection rate and classification detection rate of different types of faults of the proposed algorithm are superior to traditional algorithms. In addition, the full neural network enhancement algorithm also has significant advantages in node fault detection time and energy consumption control.