To improve the accuracy and speed of identifying node anomalies in fiber optic networks, a node anomaly identification algorithm based on clustering neural networks is proposed in this paper. Firstly, the preclassification of input data is achieved through clustering calculation, which solves the problem of traditional classification and recognition algorithms easily falling into local optima. Then, the test data grouped after preclassification is used as the input layer, and the clustering weights and clustering degrees are used as the weighting coefficients of the hidden layer to improve the recognition of abnormal signals. Experiments are conducted on 64 FBG nodes in a fiber optic network, and the temperature increment, heavy impact and periodic vibration are used to simulate the anomalous signals, respectively. The results of the comparison experiments show that the recognition accuracy of this algorithm is 80.3%, 92.8%, and 91.6% under the condition of aliasing where all three types of abnormal signals exist, which is an improvement of about 20% over the neural network algorithm without preclassification. Therefore, the present algorithm has the optimal test results in the four test cases. For the same data volume test, the speed of this algorithm is only half of that of SVM algorithm, which verifies that this algorithm has better timeliness.