Abstract: The rapid and accurate identification of abnormal information in optical fiber network communication was the key to ensuring the stability of communication. The surge in conversion of optical fiber network communication data has also become the only research hotspot. Firstly, Bayesian partition data mining was used to quantify the feature classification of data samples, and the prior probability was corrected through maximization analysis; Secondly, the mining characteristic parameter and probability coefficient were set according to different types abnormal information; Finally, according to the Bayesian partition, the sample data was collected with specific data. The experiment takes the communication state data of the optical fiber interconnection as a sample, compared the recognition results of this algorithm with the artificial neural network algorithm and the genetic algorithm, and calculated the recognition accuracy, convergence speed and algorithm stability of the three algorithms. The average value of the recognition accuracy of this algorithm was converted to 93.83%, and there was no significant decrease when the amount of data increased. The convergence speed was similar to that of genetic algorithm, with an average value of 3.25 s. The mean values of missed detection rate and false detection rate were 0.10% and 0.54%, respectively. The results show that the recognition accuracy and convergence speed of this algorithm are improved, the stability is good, and the parameter control can be fine-tuned between the missed detection rate and the false detection rate, which has better application value.