The rapid identification of abnormal data from the massive data of large-scale optical fiber networks is a key issue of optical fiber communication technology. It is also an important research direction in optimizing optical fiber communication networks and improving communication accuracy in recent years. It mainly solves constraint relationship between the monitoring accuracy and convergence speed of abnormal data. Aiming at this problem, a monitoring algorithm based on the fusion of deep learning and genetic algorithm was proposed. The segmentation preprocessing of the initial data was completed through deep learning, and then the crossover probability and mutation probability with segmentation attributes was introduced into the genetic algorithm, thereby the retention of abnormal data features were enhanced. The original data was divided according to different attributes by segmentation preprocessing, thereby the amount of initial filtering data was reducing greatly, achieving the purpose of improving the detection speed of abnormal data; the segmentation attributes was introducd into the genetic factor of the genetic algorithm to make the results have a weighting effect, the separability of data was increased, thereby improving the monitoring accuracy. The proposed algorithm was compared with unoptimized genetic algorithm and clustering algorithm in the experiment. The results showed that the minimum relative errors of abnormal data volume of proposed algorithm, traditional genetic algorithm and clustering analysis algorithm were 0.029, 0.093 and 0.104, respectively; the average deviations were respectively 0.047, 0.155 and 0.156, the average convergence time were 5.84 s, 12.6 s and 9.32 s, respectively. It can be seen that this algorithm has been well optimized in terms of monitoring accuracy, stability and timeliness.