Abstract:  To solve the problem of low accuracy in identifying abnormal data in existing methods. An abnormal data recognition algorithm for multi-dimensional feature-based communication network is proposed. The current speed and position of particles in particle swarm optimization algorithm is adjusted to obtain multi-dimensional data samples of communication network. Data features are extracted through clustering analysis in data mining, determining density indicators, and obtaining multidimensional features of the data. The extracted multidimensional features are Introduced into the deep belief network for recognition, and anomaly recognition of communication network data is achieved based on changes in feature spectrum amplitude. The experimental results show that the algorithm can effectively identify abnormal data features in communication networks and has high recognition accuracy. 

Key words: multidimensional features, data identification, particle swarm optimization, cluster analysis, deep belief network