With the rapid development of Mobile Internet and the 4th Generation mobile communication technology, data service has exceeded voice service, which has also become the important means for mobile operators to promote shares in the communication market. Therefore, the service quality of data service business will directly influence mobile user perception and satisfaction to network. With complicated process networking procedure is long in the data service process and the fundamental reasons of problems are relatively more difficult to position. During voice communication in mobile networks, there are relatively unitary important factors which can accept user perception such as call drop, network congestion and signal interference, etc. However, users’ perception towards data services is somewhat different, which shows strong association with the usage scenarios of the various applications of users. For example, in the data browsing service, if terminal connection fails, the background will start the function of automatic repeated connections, during which, latency is increased, so as to influence user perception of data service latently. Besides, in the video service, initialization delay, stalling during the play and times of stalling are also the factors which could affect video quality. The above analysis shows the latency in the various data service processes and the usual network latency indicators, such as TCP three-way handshake and DNS, etc. gathered and mapped into a total latency, which is the latency perception from the perspective of user experience. In the current work, it is defined as generalization latency, which is also known as the total latency covering latency for users to establish connection on the signaling control plane and latency of user plane.The first innovation of this paper is to establish a mapping model, where, generalization latency, which is from the perspective of user using perception, is related to performance indicators of telecommunication network, under different data service characteristic scenarios, so as to forecast the inflection point of network performance anomaly. The second innovation is to introduce the abnormally detection model for generalization latency, so as to detect the performance stability of the application layer of the application service plane.