Network entity inference is one of the important contents in cyberspace surveying and mapping. Network entity inference and calibration need to synthesize multi-source data, classify nodes in network space by synthetical judgment. Thus, the entity classification model in network space is proposed. Based on this model, a low-overhead network entity detection classification method is proposed. Firstly, for IP addresses detected by detection, the alias parsing technology is used to map multiple IP addresses belonging to a device into a network entity; Then, the decision tree is used to classify network entities in a coarse-grained manner; Finally, the Bayesian network is used to classify them in detail. Taking a city in Jiangsu Province as an example, the detection and analysis are carried out and compared with the recorded data. The experimental results show that the method can effectively classify various types of entities in the network space, thus providing support for network space map construction, situation analysis and other applications.